eBay:iPads:: startprice regression:: classtxt_sp
bdanalytics
Date: (Thu) Jul 30, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32
All.X.no.rnorm.rf: Leaderboard: 0.80638
newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.diff=77.90; idseq.my=48.49;
D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "classtxt_sp_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "startprice"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw #"sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL #function(raw) {
# return(log(raw))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
# }
# glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))
glb_map_rsp_var_to_raw <- NULL #function(var) {
# return(exp(var))
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
# }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_var <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 128 gb ", " 128gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("black\\),charger ", "black\\), charger ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" brokenCharger ", " broken Charger ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCLOUD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" marks\\.Absolutely ", " marks\\. Absolutely ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bspace (grey|gray)", "spacegray", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" UnlockedCracked ", " Unlocked Cracked ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"2nd"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","advised","antenna","anti","anyone","anything"
,"applied","area","arizona","att"
,"backlight","beetle","beginning","besides","bidder"
,"bonus","boot","bound","brick","bruises"
,"capacity","changed","changing","chrome","closely"
,"confidence","considerable","consumer","contents","control","cream","cuts"
,"daily","date","daughter"
,"deactivated","decent","defender","defense","degree"
,"demonstration","depicted","depress"
,"disclaimer","discoloration","distressed","divider"
,"dlxnqat9g5wt","dock","documents","done","dont","durable","dust","duty"
,"either","emblem","erased","ereader","esi","essentially"
,"every","exact","exhibition","expires"
,"facing","faint","february","film","final","five"
,"flickers","folding","forgot","forwarders"
,"games","generic","genuine","glitter","goes","grey","guide"
,"half","handstand","hdmi","high","higher","hole","hospital"
,"imie","immaculate","impact","instead","intended","interest","interior","intro"
,"jack","july"
,"keeps","kids","kind","known"
,"largest","last","late","let","letters","level"
,"lifting","limited","line","lining","liquidation","literally"
,"local","long","longer","looping","loose","loss"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll","mgye2ll"
,"mic","middle", "mind","mixed","mostly"
,"neither","none","november"
,"occasional","oem","often","online","outside"
,"paperwork","past","period","pet","photograph","piece","played","plug"
,"poor","portfolio","portion","pouch","preinstalled","price","proof","provided"
,"ranging","rather"
,"real","realized","reassemble","receipt","recently","red"
,"reflected","refunds","remote","repeat"
,"required","reserve","residue","restarts","result","reviewed"
,"ringer","roughly","running"
,"said","school"
,"seamlessly","seconds","seem","semi","send","september","serious","setup"
,"shell","short","site","size","sleeve","slice","smoke","smooth","smudge"
,"softer","software","somewhat","soon"
,"space","sparingly","sparkiling","special","speed"
,"stains","standup","status","stopped","strictly"
,"subtle","sustained","swappacom","swivel"
,"take","technical","tempered","texture","thank","therefore","think","though"
,"toddler","totally","touchy","toys","tried","typical"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","whole","winning","wrapped"
,"zaag","zero", "zombie","zoogue"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_txt_cor_var <- "sold" # or glb_rsp_var
glb_txt_filter_terms <- "top.cor" # select one from c("top.cor", "top.max", "sparse")
glb_txt_top_n <- c(10)
names(glb_txt_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "description", "startprice"
, "startprice.log", "sold"
)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- "sold"
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
#glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
glb_model_evl_criteria <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage")
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.773 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here")
glb_drop_obs <- c(
11234, #sold=0; 2 other dups(10306, 11503) are sold=1
11844, #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
NULL)
glb_allobs_df <- glb_allobs_df[!glb_allobs_df[, glb_id_var] %in% glb_drop_obs, ]
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
# !_sp
# spd_allobs_df <- read.csv(paste0(glb_out_pfx, "sp_predict.csv"))
# if (nrow(spd_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between spd_allobs_df & glb_allobs_df")
# mrg_allobs_df <- merge(glb_allobs_df, spd_allobs_df)
# if (nrow(mrg_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between mrg_allobs_df & glb_allobs_df")
# mrg_allobs_df$startprice.diff <- mrg_allobs_df$startprice -
# mrg_allobs_df$startprice.predict.
# print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff",
# colorcol_name = "biddable"))
# print(myplot_histogram(mrg_allobs_df, "startprice.diff",
# fill_col_name = "biddable"))
# glb_allobs_df <- mrg_allobs_df
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
# "startprice.log", "startprice.predict.")
###
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# Only for _sp
print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))##
## Test Train
## 0 0 999
## 1 0 860
## <NA> 798 0print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
useNA = "ifany"))## , , = Test
##
##
## 0 1
## 0 0 0
## 1 0 0
## <NA> 422 376
##
## , , = Train
##
##
## 0 1
## 0 802 197
## 1 220 640
## <NA> 0 0glb_allobs_df$.src <- "Test"
glb_allobs_df[!is.na(glb_allobs_df$sold) & (glb_allobs_df$sold == 1), ".src"] <- "Train"
print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))##
## Test Train
## 0 999 0
## 1 0 860
## <NA> 798 0print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
useNA = "ifany"))## , , = Test
##
##
## 0 1
## 0 802 197
## 1 0 0
## <NA> 422 376
##
## , , = Train
##
##
## 0 1
## 0 0 0
## 1 220 640
## <NA> 0 0###
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.773 13.837 3.064
## 2 inspect.data 2 0 13.838 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## [1] "feat: .rnorm"## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 13.838 16.067 2.23
## 3 scrub.data 2 1 16.068 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1593 0 0 0 0 0
## 1 288 0 4 36 28 172 196
## Unknown 4 4 2 0 0 330 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1597 0 0 0 0 0
## 1 292 0 6 36 28 172 196
## Unknown 0 0 0 0 0 330 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 16.068 16.782 0.714
## 4 transform.data 2 2 16.782 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10178, 10948, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
glb_allobs_df[row_pos, "descr.my"] <-
gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 16.782 17.38 0.598
## 5 extract.features 3 0 17.380 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 17.386 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 17.386 17.406
## 2 extract.features_factorize.str.vars 2 0 17.407 NA
## elapsed
## 1 0.02
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid prdline.my
## "storage" "productline" ".src" ".grpid" "prdline.my"
## descr.my
## "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
TfIdf_df$cor.y <- cor(TfIdf_mtrx, glb_allobs_df[, glb_txt_cor_var],
use="pairwise.complete.obs")
TfIdf_df$cor.y.abs <- abs(TfIdf_df$cor.y)
# Check all calls to get_DTM_terms to change returned order assumption
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_txt_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_stop_mtrx > 0)
tmp_allobs_df$terms.n.post.stop.log <- log(1 + tmp_allobs_df$terms.n.post.stop)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_stop_mtrx)
print(sprintf(" Top_n stem TfIDf terms for %s:", txt_var))
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_txt_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stem_mtrx
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$terms.n.post.stem.log <- log(1 + tmp_allobs_df$terms.n.post.stem)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(txt_var_pfx, c("terms.n.post.stop", "terms.n.post.stem")))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_txt_filter_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_txt_filter_terms == "top.max") {
select_terms <- orderBy(~-TfIdf,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
FALSE]
} else if (glb_txt_filter_terms == "top.cor") {
select_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
FALSE]
} else stop(
"glb_txt_filter_terms should be one of c('sparse', 'top.max', 'top.cor') vs. '",
glb_txt_filter_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <-
as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <-
as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <-
as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <-
as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: gsubfn
## Loading required package: proto
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## Loading required package: NLP
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## Attaching package: 'NLP'
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## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 17.407 18.786
## 3 extract.features_process.text 3 0 18.786 NA
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## [1] "Building glb_txt_lst..."
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(TfIdf_mtrx, glb_allobs_df[, glb_txt_cor_var], use =
## "pairwise.complete.obs"): the standard deviation is zero## [1] "Rows: 650; Cols: 6"
## TfIdf term freq pos cor.y cor.y.abs
## condition 209.3687 condition 498 135 -3.835338e-02 3.835338e-02
## new 126.4193 new 156 385 -3.857301e-02 3.857301e-02
## used 125.0496 used 240 618 1.811464e-02 1.811464e-02
## good 121.6313 good 197 257 -4.339763e-05 4.339763e-05
## scratches 114.5931 scratches 254 503 -4.043640e-03 4.043640e-03
## screen 107.3972 screen 210 505 2.527546e-02 2.527546e-02
## TfIdf term freq pos cor.y cor.y.abs
## small 33.093472 small 46 544 -0.004968545 0.004968545
## around 7.769917 around 9 53 0.004023292 0.004023292
## geek 6.790241 geek 5 247 -0.005378372 0.005378372
## unlock 6.548026 unlock 4 603 0.025004068 0.025004068
## came 4.107736 came 4 95 -0.036998269 0.036998269
## tmobile 2.425461 tmobile 2 591 0.009541038 0.009541038
## TfIdf term freq pos cor.y cor.y.abs
## 975 1.137558 975 1 15 NA NA
## blemish 1.137558 blemish 1 73 NA NA
## cables 1.137558 cables 1 94 NA NA
## engravement 1.137558 engravement 1 203 NA NA
## handling 1.137558 handling 1 269 NA NA
## 79in 1.034144 79in 1 14 -0.02152502 0.02152502
## TfIdf term freq pos cor.y cor.y.abs
## 975 1.137558 975 1 15 NA NA
## blemish 1.137558 blemish 1 73 NA NA
## cables 1.137558 cables 1 94 NA NA
## engravement 1.137558 engravement 1 203 NA NA
## handling 1.137558 handling 1 269 NA NA
## 79in 1.034144 79in 1 14 -0.02152502 0.02152502## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(TfIdf_mtrx, glb_allobs_df[, glb_txt_cor_var], use =
## "pairwise.complete.obs"): the standard deviation is zero## [1] "Rows: 510; Cols: 6"
## TfIdf term freq pos cor.y cor.y.abs
## condit 209.3578 condit 496 109 -0.0370979676 0.0370979676
## use 148.2548 use 291 483 0.0145826883 0.0145826883
## scratch 129.1148 scratch 286 391 -0.0075325507 0.0075325507
## new 126.4193 new 156 299 -0.0385730073 0.0385730073
## good 121.7207 good 197 202 -0.0002501726 0.0002501726
## ipad 108.9895 ipad 232 235 -0.0123064552 0.0123064552
## TfIdf term freq pos cor.y cor.y.abs
## ding 21.771743 ding 25 145 0.01622488 0.01622488
## came 4.107736 came 4 83 -0.03699827 0.03699827
## freez 3.791861 freez 1 185 NA NA
## complet 3.423508 complet 4 107 0.04994396 0.04994396
## definit 2.848180 definit 3 135 -0.03044917 0.03044917
## greet 2.075117 greet 2 206 NA NA
## TfIdf term freq pos cor.y cor.y.abs
## 511 1.421948 511 1 11 -0.02152502 0.02152502
## attach 1.421948 attach 1 48 0.02500407 0.02500407
## binder 1.421948 binder 1 63 -0.02152502 0.02152502
## 360 1.263954 360 1 9 0.02500407 0.02500407
## 975 1.137558 975 1 15 NA NA
## 79in 1.034144 79in 1 14 -0.02152502 0.02152502
## TfIdf term freq pos cor.y cor.y.abs
## 511 1.421948 511 1 11 -0.02152502 0.02152502
## attach 1.421948 attach 1 48 0.02500407 0.02500407
## binder 1.421948 binder 1 63 -0.02152502 0.02152502
## 360 1.263954 360 1 9 0.02500407 0.02500407
## 975 1.137558 975 1 15 NA NA
## 79in 1.034144 79in 1 14 -0.02152502 0.02152502## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.n.post.stop -0.08553363
## terms.n.post.stop.log -0.10476279
## TfIdf.sum.post.stop -0.12336522
## terms.n.post.stem -0.08487552
## terms.n.post.stem.log -0.10449569
## TfIdf.sum.post.stem -0.12075404
## terms.n.stem.stop.Ratio 0.04157435
## TfIdf.sum.stem.stop.Ratio 0.09980407
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 20.859 31.865
## 6 extract.features_extract.DTM 5 0 31.865 NA
## elapsed
## 5 11.006
## 6 NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 31.865 33.729 1.864
## 7 extract.features_report.DTM 6 0 33.730 NA NA## Loading required package: reshape2## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 510)>>
## Non-/sparse entries: 8197/1346873
## Sparsity : 99%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in cor(TfIdf_mtrx, glb_allobs_df[, glb_txt_cor_var], use =
## "pairwise.complete.obs"): the standard deviation is zero## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 8)>>
## Non-/sparse entries: 2069/19187
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 33.730 36.056 2.326
## 8 extract.features_bind.DTM 7 0 36.057 NA NA
## [1] "Binding DTM for descr.my..."## Warning in cor(TfIdf_mtrx, glb_allobs_df[, glb_txt_cor_var], use =
## "pairwise.complete.obs"): the standard deviation is zero## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 36.057 36.549 0.493
## 9 extract.features_bind.DXM 8 0 36.550 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# Use model info provided in description
mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)## UniqueID startprice prdline.my sold .grpid color condition cellular
## 618 10618 100 iPad mini 1 <NA> Black Used 0
## 940 10940 350 iPad 3 0 <NA> Black Used 1
## 2472 12474 190 Unknown NA <NA> Unknown Used Unknown
## carrier storage
## 618 None 16
## 940 Verizon 16
## 2472 Unknown Unknown
## descr.my
## 618 Nice Apple iPad Mini 16GB Wi- Fi 7.9" spacegray MF432LL/ A A1432 Locked It does work just cannot
## 940 LIKE NEW (MODEL A1430) + BLUETOOTH KEYBOARD (LATEST MODEL A1314), LEATHER CREAM SMART COVER, BLACK
## 2472 here we have spacegray apple ipad mini a1432 no charger works great has small nicks nothing majorglb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)## UniqueID startprice prdline.my sold .grpid color
## 617 10617 0.99 iPad 2 1 <NA> White
## 618 10618 100.00 iPad mini 1 <NA> Black
## 992 10992 110.00 iPad 2 0 <NA> White
## 1105 11105 303.67 iPad mini Retina 0 <NA> Gold
## 1359 11360 200.00 iPad 3 0 <NA> Unknown
## 1360 11361 0.99 Unknown 1 <NA> Unknown
## 1365 11366 125.00 iPad 1 1 <NA> Unknown
## 2637 12639 49.99 iPad 2 NA <NA> Black
## condition cellular carrier storage
## 617 Used 0 None 64
## 618 Used 0 None 16
## 992 Used 0 None 16
## 1105 Used 0 None 16
## 1359 Used Unknown Unknown Unknown
## 1360 Used Unknown Unknown Unknown
## 1365 Used Unknown Unknown Unknown
## 2637 For parts or not working 0 None 64
## descr.my
## 617 This a used Apple iPad 2 64GB, Wi- Fi, 9.7in - White (MC991LL/ A) shows signs of wear, has been
## 618 Nice Apple iPad Mini 16GB Wi- Fi 7.9" spacegray MF432LL/ A A1432 Locked It does work just cannot
## 992 Up for auction is this APPLE iPad 1st Gen Model MB292LL 16 GB of Memory Storage 9.7" touch screen
## 1105 Like New Condition Apple iPad Mini 3 MGYE2LL/ A 16GB Wi- Fi Gold Version Tablet/ eReader. Includes USB
## 1359 iPad 3 Black 64Gb storage Model Mc707ll/ a iPad is in very nice shape, glass and case
## 1360 APPLE iPAD AIR 32GB WHITE MD789LL/ B WHITE. This item is Previously Lightly Used, in Good Condition.
## 1365 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 2637 IPAD 2 64GB BLACK MODEL MC916LL/ A WIFI ONLY MODEL. PICTURE OF IPAD IS ACTUAL UNIT YOU WILL RECEIVE.glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"
# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
glb_rsp_var)))## prdline.my productline D.P.mini startprice .n
## 1 iPad 2 iPad 2 0 0.99 38
## 2 iPad mini iPad mini 0 0.99 30
## 3 iPad 1 iPad 1 0 0.99 26
## 4 Unknown Unknown 0 0.99 25
## 5 iPad 1 iPad 1 0 50.00 22
## 6 iPad mini iPad mini 0 150.00 20
## 7 iPad Air iPad Air 0 0.99 17
## 8 iPad 2 iPad 2 0 150.00 16
## 9 iPad 4 iPad 4 0 0.99 15
## 10 iPad mini iPad mini 0 100.00 14
## 11 iPad 2 iPad 2 0 100.00 13
## 12 iPad Air 2 iPad Air 2 0 0.99 13
## 13 iPad mini 2 iPad mini 2 0 0.99 13
## 14 iPad 1 iPad 1 0 80.00 12
## 15 iPad 3 iPad 3 0 0.99 12
## 16 iPad 3 iPad 3 0 200.00 12
## 17 iPad 1 iPad 1 0 90.00 11
## 18 iPad 2 iPad 2 0 175.00 11
## 19 Unknown Unknown 0 150.00 10
## 20 iPad 1 iPad 1 0 75.00 10
## 21 iPad 1 iPad 1 0 100.00 10
## 22 iPad 2 iPad 2 0 0.01 10
## 23 iPad 3 iPad 3 0 250.00 10
## 24 iPad mini iPad mini 0 50.00 10
## 25 iPad mini iPad mini 0 99.99 10
## 26 Unknown Unknown 0 100.00 9
## 27 iPad 2 iPad 2 0 99.99 9
## 28 iPad 2 iPad 2 0 149.99 9
## 29 iPad 2 iPad 2 0 199.99 9
## 30 iPad Air iPad Air 0 300.00 9
## 31 iPad mini iPad mini 0 199.99 9
## 32 Unknown Unknown 0 300.00 8
## 33 iPad 1 iPad 1 0 95.00 8
## 34 iPad 2 iPad 2 0 99.00 8
## 35 iPad 2 iPad 2 0 125.00 8
## 36 iPad 2 iPad 2 0 200.00 8
## 37 iPad 4 iPad 4 0 249.99 8
## 38 iPad Air 2 iPad Air 2 0 550.00 8
## 39 iPad mini iPad mini 0 200.00 8
## 40 iPad mini 2 iPad mini 2 0 350.00 8
## 41 Unknown Unknown 0 50.00 7
## 42 iPad 1 iPad 1 0 70.00 7
## 43 iPad 2 iPad 2 0 9.99 7
## 44 iPad 2 iPad 2 0 75.00 7
## 45 iPad 2 iPad 2 0 180.00 7
## 46 iPad 4 iPad 4 0 199.99 7
## 47 iPad mini iPad mini 0 99.00 7
## 48 iPad mini 3 iPad mini 3 0 0.99 7
## 49 iPad 1 iPad 1 0 1.00 6
## 50 iPad 2 iPad 2 0 50.00 6
## 51 iPad 2 iPad 2 0 160.00 6
## 52 iPad 4 iPad 4 0 100.00 6
## 53 iPad 4 iPad 4 0 150.00 6
## 54 iPad 4 iPad 4 0 279.99 6
## 55 iPad Air iPad Air 0 1.00 6
## 56 iPad Air iPad Air 0 200.00 6
## 57 iPad Air iPad Air 0 400.00 6
## 58 iPad Air 2 iPad Air 2 0 450.00 6
## 59 iPad mini iPad mini 0 75.00 6
## 60 iPad mini iPad mini 0 89.99 6
## 61 iPad mini iPad mini 0 159.99 6
## 62 iPad mini iPad mini 0 175.00 6
## 63 iPad 1 iPad 1 0 29.99 5
## 64 iPad 1 iPad 1 0 55.00 5
## 65 iPad 1 iPad 1 0 79.99 5
## 66 iPad 1 iPad 1 0 99.00 5
## 67 iPad 2 iPad 2 0 80.00 5
## 68 iPad 2 iPad 2 0 165.00 5
## 69 iPad 2 iPad 2 0 179.00 5
## 70 iPad 3 iPad 3 0 99.00 5
## 71 iPad 3 iPad 3 0 150.00 5
## 72 iPad 3 iPad 3 0 220.00 5
## 73 iPad 3 iPad 3 0 225.00 5
## 74 iPad 3 iPad 3 0 300.00 5
## 75 iPad 4 iPad 4 0 250.00 5
## 76 iPad 4 iPad 4 0 400.00 5
## 77 iPad Air iPad Air 0 100.00 5
## 78 iPad Air iPad Air 0 250.00 5
## 79 iPad Air iPad Air 0 350.00 5
## 80 iPad Air iPad Air 0 389.99 5
## 81 iPad Air 2 iPad Air 2 0 499.99 5
## 82 iPad mini iPad mini 0 1.00 5
## 83 iPad mini iPad mini 0 250.00 5
## 84 iPad mini iPad mini 0 350.00 5
## 85 iPad mini 2 iPad mini 2 0 200.00 5
## 86 iPad mini 2 iPad mini 2 0 225.00 5
## 87 Unknown Unknown 0 25.00 4
## 88 Unknown Unknown 0 149.99 4
## 89 Unknown Unknown 0 250.00 4
## 90 iPad 1 iPad 1 0 40.00 4
## 91 iPad 1 iPad 1 0 49.99 4
## 92 iPad 1 iPad 1 0 79.00 4
## 93 iPad 1 iPad 1 0 105.00 4
## 94 iPad 1 iPad 1 0 110.00 4
## 95 iPad 2 iPad 2 0 1.00 4
## 96 iPad 2 iPad 2 0 40.00 4
## 97 iPad 2 iPad 2 0 49.99 4
## 98 iPad 2 iPad 2 0 130.00 4
## 99 iPad 2 iPad 2 0 140.00 4
## 100 iPad 2 iPad 2 0 155.00 4
## 101 iPad 2 iPad 2 0 164.99 4
## 102 iPad 2 iPad 2 0 174.99 4
## 103 iPad 2 iPad 2 0 179.99 4
## 104 iPad 2 iPad 2 0 189.99 4
## 105 iPad 2 iPad 2 0 250.00 4
## 106 iPad 3 iPad 3 0 9.99 4
## 107 iPad 3 iPad 3 0 100.00 4
## 108 iPad 3 iPad 3 0 149.99 4
## 109 iPad 3 iPad 3 0 175.00 4
## 110 iPad 3 iPad 3 0 199.99 4
## 111 iPad 3 iPad 3 0 219.99 4
## 112 iPad 3 iPad 3 0 249.99 4
## 113 iPad 3 iPad 3 0 275.00 4
## 114 iPad 4 iPad 4 0 0.01 4
## 115 iPad 4 iPad 4 0 99.99 4
## 116 iPad 4 iPad 4 0 200.00 4
## 117 iPad 4 iPad 4 0 299.00 4
## 118 iPad Air iPad Air 0 199.99 4
## 119 iPad Air iPad Air 0 229.00 4
## 120 iPad Air iPad Air 0 279.99 4
## 121 iPad Air iPad Air 0 325.00 4
## 122 iPad Air iPad Air 0 329.99 4
## 123 iPad Air iPad Air 0 500.00 4
## 124 iPad Air 2 iPad Air 2 0 250.00 4
## 125 iPad Air 2 iPad Air 2 0 350.00 4
## 126 iPad Air 2 iPad Air 2 0 399.00 4
## 127 iPad Air 2 iPad Air 2 0 399.99 4
## 128 iPad Air 2 iPad Air 2 0 400.00 4
## 129 iPad Air 2 iPad Air 2 0 499.00 4
## 130 iPad Air 2 iPad Air 2 0 500.00 4
## 131 iPad Air 2 iPad Air 2 0 549.99 4
## 132 iPad mini iPad mini 0 119.99 4
## 133 iPad mini iPad mini 0 130.00 4
## 134 iPad mini iPad mini 0 199.00 4
## 135 iPad mini iPad mini 0 275.00 4
## 136 iPad mini iPad mini 0 300.00 4
## 137 iPad mini iPad mini 1 0.99 4
## 138 iPad mini 2 iPad mini 2 0 175.00 4
## 139 iPad mini 2 iPad mini 2 0 250.00 4
## 140 iPad mini 3 iPad mini 3 0 325.00 4
## 141 iPad mini 3 iPad mini 3 0 499.99 4
## 142 iPad mini 3 iPad mini 3 0 599.99 4
## 143 Unknown Unknown 0 15.00 3
## 144 Unknown Unknown 0 40.00 3
## 145 Unknown Unknown 0 75.00 3
## 146 Unknown Unknown 0 99.00 3
## 147 Unknown Unknown 0 120.00 3
## 148 Unknown Unknown 0 199.00 3
## 149 Unknown Unknown 0 199.99 3
## 150 Unknown Unknown 0 200.00 3
## 151 Unknown Unknown 0 249.00 3
## 152 Unknown Unknown 0 249.99 3
## 153 Unknown Unknown 0 299.99 3
## 154 Unknown Unknown 0 319.00 3
## 155 Unknown Unknown 0 350.00 3
## 156 iPad 1 iPad 1 0 0.01 3
## 157 iPad 1 iPad 1 0 19.99 3
## 158 iPad 1 iPad 1 0 20.00 3
## 159 iPad 1 iPad 1 0 25.00 3
## 160 iPad 1 iPad 1 0 30.00 3
## 161 iPad 1 iPad 1 0 36.95 3
## 162 iPad 1 iPad 1 0 65.00 3
## 163 iPad 1 iPad 1 0 84.99 3
## 164 iPad 1 iPad 1 0 85.00 3
## 165 iPad 1 iPad 1 0 89.00 3
## 166 iPad 1 iPad 1 0 99.99 3
## 167 iPad 1 iPad 1 0 119.99 3
## 168 iPad 1 iPad 1 0 150.00 3
## 169 iPad 1 iPad 1 0 180.00 3
## 170 iPad 2 iPad 2 0 30.00 3
## 171 iPad 2 iPad 2 0 70.00 3
## 172 iPad 2 iPad 2 0 85.00 3
## 173 iPad 2 iPad 2 0 89.99 3
## 174 iPad 2 iPad 2 0 90.00 3
## 175 iPad 2 iPad 2 0 120.00 3
## 176 iPad 2 iPad 2 0 129.95 3
## 177 iPad 2 iPad 2 0 129.99 3
## 178 iPad 2 iPad 2 0 139.00 3
## 179 iPad 2 iPad 2 0 149.00 3
## 180 iPad 2 iPad 2 0 149.95 3
## 181 iPad 2 iPad 2 0 154.00 3
## 182 iPad 2 iPad 2 0 159.99 3
## 183 iPad 2 iPad 2 0 169.00 3
## 184 iPad 2 iPad 2 0 249.97 3
## 185 iPad 2 iPad 2 0 275.00 3
## 186 iPad 2 iPad 2 0 300.00 3
## 187 iPad 3 iPad 3 0 1.00 3
## 188 iPad 3 iPad 3 0 10.00 3
## 189 iPad 3 iPad 3 0 99.99 3
## 190 iPad 3 iPad 3 0 128.00 3
## 191 iPad 3 iPad 3 0 185.00 3
## 192 iPad 3 iPad 3 0 187.50 3
## 193 iPad 3 iPad 3 0 199.00 3
## 194 iPad 4 iPad 4 0 50.00 3
## 195 iPad 4 iPad 4 0 225.00 3
## 196 iPad 4 iPad 4 0 259.99 3
## 197 iPad 4 iPad 4 0 275.00 3
## 198 iPad 4 iPad 4 0 280.00 3
## 199 iPad 4 iPad 4 0 300.00 3
## 200 iPad 4 iPad 4 0 320.00 3
## 201 iPad Air iPad Air 0 90.00 3
## 202 iPad Air iPad Air 0 290.00 3
## 203 iPad Air iPad Air 0 299.99 3
## 204 iPad Air iPad Air 0 320.00 3
## 205 iPad Air iPad Air 0 349.00 3
## 206 iPad Air iPad Air 0 379.00 3
## 207 iPad Air iPad Air 0 415.00 3
## 208 iPad Air iPad Air 0 449.99 3
## 209 iPad Air 2 iPad Air 2 0 1.00 3
## 210 iPad Air 2 iPad Air 2 0 50.00 3
## 211 iPad Air 2 iPad Air 2 0 199.99 3
## 212 iPad Air 2 iPad Air 2 0 425.00 3
## 213 iPad Air 2 iPad Air 2 0 439.99 3
## 214 iPad Air 2 iPad Air 2 0 480.00 3
## 215 iPad Air 2 iPad Air 2 0 525.00 3
## 216 iPad Air 2 iPad Air 2 0 560.00 3
## 217 iPad mini iPad mini 0 0.01 3
## 218 iPad mini iPad mini 0 20.00 3
## 219 iPad mini iPad mini 0 25.00 3
## 220 iPad mini iPad mini 0 45.00 3
## 221 iPad mini iPad mini 0 60.00 3
## 222 iPad mini iPad mini 0 125.00 3
## 223 iPad mini iPad mini 0 149.00 3
## 224 iPad mini iPad mini 0 160.00 3
## 225 iPad mini iPad mini 0 179.99 3
## 226 iPad mini iPad mini 0 189.99 3
## 227 iPad mini iPad mini 0 210.00 3
## 228 iPad mini iPad mini 0 249.99 3
## 229 iPad mini iPad mini 0 259.99 3
## 230 iPad mini iPad mini 0 290.00 3
## 231 iPad mini iPad mini 0 400.00 3
## 232 iPad mini 2 iPad mini 2 0 100.00 3
## 233 iPad mini 2 iPad mini 2 0 120.00 3
## 234 iPad mini 2 iPad mini 2 0 180.00 3
## 235 iPad mini 2 iPad mini 2 0 285.00 3
## 236 iPad mini 2 iPad mini 2 0 300.00 3
## 237 iPad mini 2 iPad mini 2 0 375.00 3
## 238 iPad mini 3 iPad mini 3 0 99.00 3
## 239 iPad mini 3 iPad mini 3 0 300.00 3
## 240 iPad mini 3 iPad mini 3 0 329.99 3
## 241 iPad mini 3 iPad mini 3 0 350.00 3
## 242 iPad mini 3 iPad mini 3 0 399.99 3
## 243 iPad mini 3 iPad mini 3 0 400.00 3
## 244 iPad mini 3 iPad mini 3 0 449.99 3
## 245 iPad mini 3 iPad mini 3 0 729.99 3
## 246 Unknown Unknown 0 5.00 2
## 247 Unknown Unknown 0 9.99 2
## 248 Unknown Unknown 0 19.99 2
## 249 Unknown Unknown 0 20.00 2
## 250 Unknown Unknown 0 39.99 2
## 251 Unknown Unknown 0 70.00 2
## 252 Unknown Unknown 0 79.95 2
## 253 Unknown Unknown 0 80.00 2
## 254 Unknown Unknown 0 99.99 2
## 255 Unknown Unknown 0 108.00 2
## 256 Unknown Unknown 0 159.99 2
## 257 Unknown Unknown 0 165.00 2
## 258 Unknown Unknown 0 169.99 2
## 259 Unknown Unknown 0 175.00 2
## 260 Unknown Unknown 0 185.00 2
## 261 Unknown Unknown 0 280.00 2
## 262 Unknown Unknown 0 319.99 2
## 263 Unknown Unknown 0 375.00 2
## 264 Unknown Unknown 0 399.00 2
## 265 Unknown Unknown 0 450.00 2
## 266 Unknown Unknown 0 500.00 2
## 267 Unknown Unknown 0 550.00 2
## 268 Unknown Unknown 0 599.99 2
## 269 Unknown Unknown 0 700.00 2
## 270 Unknown Unknown 1 149.99 2
## 271 iPad 1 iPad 1 0 9.50 2
## 272 iPad 1 iPad 1 0 9.99 2
## 273 iPad 1 iPad 1 0 10.00 2
## 274 iPad 1 iPad 1 0 14.99 2
## 275 iPad 1 iPad 1 0 15.00 2
## 276 iPad 1 iPad 1 0 45.00 2
## 277 iPad 1 iPad 1 0 58.00 2
## 278 iPad 1 iPad 1 0 60.00 2
## 279 iPad 1 iPad 1 0 62.00 2
## 280 iPad 1 iPad 1 0 69.00 2
## 281 iPad 1 iPad 1 0 69.99 2
## 282 iPad 1 iPad 1 0 89.95 2
## 283 iPad 1 iPad 1 0 92.14 2
## 284 iPad 1 iPad 1 0 101.00 2
## 285 iPad 1 iPad 1 0 104.99 2
## 286 iPad 1 iPad 1 0 115.00 2
## 287 iPad 1 iPad 1 0 124.95 2
## 288 iPad 1 iPad 1 0 125.00 2
## 289 iPad 1 iPad 1 0 129.99 2
## 290 iPad 1 iPad 1 0 165.00 2
## 291 iPad 1 iPad 1 0 175.00 2
## 292 iPad 1 iPad 1 0 250.00 2
## 293 iPad 1 iPad 1 0 279.95 2
## 294 iPad 2 iPad 2 0 0.10 2
## 295 iPad 2 iPad 2 0 15.00 2
## 296 iPad 2 iPad 2 0 19.95 2
## 297 iPad 2 iPad 2 0 59.99 2
## 298 iPad 2 iPad 2 0 65.00 2
## 299 iPad 2 iPad 2 0 69.99 2
## 300 iPad 2 iPad 2 0 74.99 2
## 301 iPad 2 iPad 2 0 89.00 2
## 302 iPad 2 iPad 2 0 95.00 2
## 303 iPad 2 iPad 2 0 119.99 2
## 304 iPad 2 iPad 2 0 128.00 2
## 305 iPad 2 iPad 2 0 135.00 2
## 306 iPad 2 iPad 2 0 144.99 2
## 307 iPad 2 iPad 2 0 145.00 2
## 308 iPad 2 iPad 2 0 149.97 2
## 309 iPad 2 iPad 2 0 150.99 2
## 310 iPad 2 iPad 2 0 162.00 2
## 311 iPad 2 iPad 2 0 169.99 2
## 312 iPad 2 iPad 2 0 170.00 2
## 313 iPad 2 iPad 2 0 172.00 2
## 314 iPad 2 iPad 2 0 179.95 2
## 315 iPad 2 iPad 2 0 204.00 2
## 316 iPad 2 iPad 2 0 220.00 2
## 317 iPad 2 iPad 2 0 350.00 2
## 318 iPad 3 iPad 3 0 0.01 2
## 319 iPad 3 iPad 3 0 25.00 2
## 320 iPad 3 iPad 3 0 49.99 2
## 321 iPad 3 iPad 3 0 89.99 2
## 322 iPad 3 iPad 3 0 99.95 2
## 323 iPad 3 iPad 3 0 125.00 2
## 324 iPad 3 iPad 3 0 140.00 2
## 325 iPad 3 iPad 3 0 179.99 2
## 326 iPad 3 iPad 3 0 180.00 2
## 327 iPad 3 iPad 3 0 209.99 2
## 328 iPad 3 iPad 3 0 215.00 2
## 329 iPad 3 iPad 3 0 229.99 2
## 330 iPad 3 iPad 3 0 239.88 2
## 331 iPad 3 iPad 3 0 239.99 2
## 332 iPad 3 iPad 3 0 299.00 2
## 333 iPad 3 iPad 3 0 314.99 2
## 334 iPad 3 iPad 3 0 450.00 2
## 335 iPad 4 iPad 4 0 80.00 2
## 336 iPad 4 iPad 4 0 99.98 2
## 337 iPad 4 iPad 4 0 107.00 2
## 338 iPad 4 iPad 4 0 125.00 2
## 339 iPad 4 iPad 4 0 195.00 2
## 340 iPad 4 iPad 4 0 199.00 2
## 341 iPad 4 iPad 4 0 209.00 2
## 342 iPad 4 iPad 4 0 240.00 2
## 343 iPad 4 iPad 4 0 255.00 2
## 344 iPad 4 iPad 4 0 265.00 2
## 345 iPad 4 iPad 4 0 269.99 2
## 346 iPad 4 iPad 4 0 285.00 2
## 347 iPad 4 iPad 4 0 295.00 2
## 348 iPad 4 iPad 4 0 299.99 2
## 349 iPad 4 iPad 4 0 305.00 2
## 350 iPad 4 iPad 4 0 309.99 2
## 351 iPad 4 iPad 4 0 310.00 2
## 352 iPad 4 iPad 4 0 315.00 2
## 353 iPad 4 iPad 4 0 324.99 2
## 354 iPad 4 iPad 4 0 325.00 2
## 355 iPad 4 iPad 4 0 344.00 2
## 356 iPad 4 iPad 4 0 350.00 2
## 357 iPad 4 iPad 4 0 367.97 2
## 358 iPad 4 iPad 4 0 375.00 2
## 359 iPad 4 iPad 4 0 500.00 2
## 360 iPad 4 iPad 4 0 588.18 2
## 361 iPad Air iPad Air 0 49.99 2
## 362 iPad Air iPad Air 0 75.00 2
## 363 iPad Air iPad Air 0 89.99 2
## 364 iPad Air iPad Air 0 99.99 2
## 365 iPad Air iPad Air 0 149.99 2
## 366 iPad Air iPad Air 0 199.00 2
## 367 iPad Air iPad Air 0 209.00 2
## 368 iPad Air iPad Air 0 245.00 2
## 369 iPad Air iPad Air 0 249.98 2
## 370 iPad Air iPad Air 0 265.00 2
## 371 iPad Air iPad Air 0 279.00 2
## 372 iPad Air iPad Air 0 280.00 2
## 373 iPad Air iPad Air 0 299.00 2
## 374 iPad Air iPad Air 0 319.00 2
## 375 iPad Air iPad Air 0 319.95 2
## 376 iPad Air iPad Air 0 319.99 2
## 377 iPad Air iPad Air 0 320.99 2
## 378 iPad Air iPad Air 0 339.00 2
## 379 iPad Air iPad Air 0 349.99 2
## 380 iPad Air iPad Air 0 369.99 2
## 381 iPad Air iPad Air 0 375.00 2
## 382 iPad Air iPad Air 0 379.99 2
## 383 iPad Air iPad Air 0 398.99 2
## 384 iPad Air iPad Air 0 399.99 2
## 385 iPad Air iPad Air 0 450.00 2
## 386 iPad Air iPad Air 0 579.99 2
## 387 iPad Air iPad Air 0 648.00 2
## 388 iPad Air iPad Air 0 750.00 2
## 389 iPad Air 2 iPad Air 2 0 99.99 2
## 390 iPad Air 2 iPad Air 2 0 200.00 2
## 391 iPad Air 2 iPad Air 2 0 260.00 2
## 392 iPad Air 2 iPad Air 2 0 300.00 2
## 393 iPad Air 2 iPad Air 2 0 349.99 2
## 394 iPad Air 2 iPad Air 2 0 379.99 2
## 395 iPad Air 2 iPad Air 2 0 419.00 2
## 396 iPad Air 2 iPad Air 2 0 449.00 2
## 397 iPad Air 2 iPad Air 2 0 465.99 2
## 398 iPad Air 2 iPad Air 2 0 475.00 2
## 399 iPad Air 2 iPad Air 2 0 549.00 2
## 400 iPad Air 2 iPad Air 2 0 559.99 2
## 401 iPad Air 2 iPad Air 2 0 575.00 2
## 402 iPad Air 2 iPad Air 2 0 639.00 2
## 403 iPad Air 2 iPad Air 2 0 639.99 2
## 404 iPad Air 2 iPad Air 2 0 650.00 2
## 405 iPad Air 2 iPad Air 2 0 729.99 2
## 406 iPad Air 2 iPad Air 2 0 749.00 2
## 407 iPad Air 2 iPad Air 2 0 749.95 2
## 408 iPad Air 2 iPad Air 2 0 800.00 2
## 409 iPad mini iPad mini 0 5.00 2
## 410 iPad mini iPad mini 0 10.00 2
## 411 iPad mini iPad mini 0 30.00 2
## 412 iPad mini iPad mini 0 70.00 2
## 413 iPad mini iPad mini 0 85.00 2
## 414 iPad mini iPad mini 0 90.00 2
## 415 iPad mini iPad mini 0 99.95 2
## 416 iPad mini iPad mini 0 114.99 2
## 417 iPad mini iPad mini 0 115.00 2
## 418 iPad mini iPad mini 0 139.99 2
## 419 iPad mini iPad mini 0 155.00 2
## 420 iPad mini iPad mini 0 165.00 2
## 421 iPad mini iPad mini 0 174.99 2
## 422 iPad mini iPad mini 0 215.00 2
## 423 iPad mini iPad mini 0 219.99 2
## 424 iPad mini iPad mini 0 225.00 2
## 425 iPad mini iPad mini 0 230.00 2
## 426 iPad mini iPad mini 0 239.99 2
## 427 iPad mini iPad mini 0 249.00 2
## 428 iPad mini iPad mini 0 258.98 2
## 429 iPad mini iPad mini 0 280.00 2
## 430 iPad mini iPad mini 0 285.00 2
## 431 iPad mini iPad mini 0 299.99 2
## 432 iPad mini iPad mini 1 179.00 2
## 433 iPad mini iPad mini 1 199.00 2
## 434 iPad mini 2 iPad mini 2 0 1.00 2
## 435 iPad mini 2 iPad mini 2 0 99.00 2
## 436 iPad mini 2 iPad mini 2 0 99.99 2
## 437 iPad mini 2 iPad mini 2 0 187.99 2
## 438 iPad mini 2 iPad mini 2 0 230.00 2
## 439 iPad mini 2 iPad mini 2 0 235.00 2
## 440 iPad mini 2 iPad mini 2 0 269.00 2
## 441 iPad mini 2 iPad mini 2 0 275.00 2
## 442 iPad mini 2 iPad mini 2 0 280.00 2
## 443 iPad mini 2 iPad mini 2 0 289.00 2
## 444 iPad mini 2 iPad mini 2 0 299.00 2
## 445 iPad mini 2 iPad mini 2 0 315.00 2
## 446 iPad mini 2 iPad mini 2 0 325.00 2
## 447 iPad mini 2 iPad mini 2 0 329.00 2
## 448 iPad mini 2 iPad mini 2 0 329.99 2
## 449 iPad mini 2 iPad mini 2 0 349.99 2
## 450 iPad mini 2 iPad mini 2 0 399.99 2
## 451 iPad mini 2 iPad mini 2 0 499.00 2
## 452 iPad mini 3 iPad mini 3 0 0.01 2
## 453 iPad mini 3 iPad mini 3 0 199.00 2
## 454 iPad mini 3 iPad mini 3 0 284.99 2
## 455 iPad mini 3 iPad mini 3 0 299.99 2
## 456 iPad mini 3 iPad mini 3 0 345.00 2
## 457 iPad mini 3 iPad mini 3 0 349.00 2
## 458 iPad mini 3 iPad mini 3 0 375.00 2
## 459 iPad mini 3 iPad mini 3 0 389.99 2
## 460 iPad mini 3 iPad mini 3 0 498.88 2
## 461 iPad mini 3 iPad mini 3 0 500.00 2
## 462 Unknown Unknown 0 0.01 1
## 463 Unknown Unknown 0 1.00 1
## 464 Unknown Unknown 0 2.99 1
## 465 Unknown Unknown 0 3.99 1
## 466 Unknown Unknown 0 4.69 1
## 467 Unknown Unknown 0 4.99 1
## 468 Unknown Unknown 0 5.65 1
## 469 Unknown Unknown 0 7.99 1
## 470 Unknown Unknown 0 8.99 1
## 471 Unknown Unknown 0 9.95 1
## 472 Unknown Unknown 0 10.00 1
## 473 Unknown Unknown 0 14.00 1
## 474 Unknown Unknown 0 14.49 1
## 475 Unknown Unknown 0 14.99 1
## 476 Unknown Unknown 0 17.75 1
## 477 Unknown Unknown 0 24.99 1
## 478 Unknown Unknown 0 28.00 1
## 479 Unknown Unknown 0 28.75 1
## 480 Unknown Unknown 0 30.00 1
## 481 Unknown Unknown 0 30.99 1
## 482 Unknown Unknown 0 32.95 1
## 483 Unknown Unknown 0 35.00 1
## 484 Unknown Unknown 0 37.98 1
## 485 Unknown Unknown 0 41.00 1
## 486 Unknown Unknown 0 43.20 1
## 487 Unknown Unknown 0 44.99 1
## 488 Unknown Unknown 0 45.00 1
## 489 Unknown Unknown 0 49.95 1
## 490 Unknown Unknown 0 49.99 1
## 491 Unknown Unknown 0 51.99 1
## 492 Unknown Unknown 0 52.25 1
## 493 Unknown Unknown 0 57.50 1
## 494 Unknown Unknown 0 58.00 1
## 495 Unknown Unknown 0 59.95 1
## 496 Unknown Unknown 0 59.99 1
## 497 Unknown Unknown 0 63.00 1
## 498 Unknown Unknown 0 65.00 1
## 499 Unknown Unknown 0 69.00 1
## 500 Unknown Unknown 0 70.99 1
## 501 Unknown Unknown 0 76.00 1
## 502 Unknown Unknown 0 87.00 1
## 503 Unknown Unknown 0 89.00 1
## 504 Unknown Unknown 0 90.00 1
## 505 Unknown Unknown 0 91.00 1
## 506 Unknown Unknown 0 99.46 1
## 507 Unknown Unknown 0 109.99 1
## 508 Unknown Unknown 0 110.00 1
## 509 Unknown Unknown 0 114.48 1
## 510 Unknown Unknown 0 116.33 1
## 511 Unknown Unknown 0 118.84 1
## 512 Unknown Unknown 0 118.95 1
## 513 Unknown Unknown 0 119.99 1
## 514 Unknown Unknown 0 124.99 1
## 515 Unknown Unknown 0 125.00 1
## 516 Unknown Unknown 0 127.95 1
## 517 Unknown Unknown 0 137.95 1
## 518 Unknown Unknown 0 139.00 1
## 519 Unknown Unknown 0 139.99 1
## 520 Unknown Unknown 0 145.00 1
## 521 Unknown Unknown 0 149.98 1
## 522 Unknown Unknown 0 150.87 1
## 523 Unknown Unknown 0 154.99 1
## 524 Unknown Unknown 0 155.00 1
## 525 Unknown Unknown 0 167.38 1
## 526 Unknown Unknown 0 168.00 1
## 527 Unknown Unknown 0 170.00 1
## 528 Unknown Unknown 0 174.00 1
## 529 Unknown Unknown 0 177.99 1
## 530 Unknown Unknown 0 179.99 1
## 531 Unknown Unknown 0 180.00 1
## 532 Unknown Unknown 0 182.77 1
## 533 Unknown Unknown 0 186.00 1
## 534 Unknown Unknown 0 188.00 1
## 535 Unknown Unknown 0 190.99 1
## 536 Unknown Unknown 0 193.15 1
## 537 Unknown Unknown 0 195.00 1
## 538 Unknown Unknown 0 196.79 1
## 539 Unknown Unknown 0 199.97 1
## 540 Unknown Unknown 0 210.00 1
## 541 Unknown Unknown 0 214.95 1
## 542 Unknown Unknown 0 215.00 1
## 543 Unknown Unknown 0 220.00 1
## 544 Unknown Unknown 0 224.00 1
## 545 Unknown Unknown 0 225.00 1
## 546 Unknown Unknown 0 229.00 1
## 547 Unknown Unknown 0 229.95 1
## 548 Unknown Unknown 0 229.99 1
## 549 Unknown Unknown 0 230.00 1
## 550 Unknown Unknown 0 232.99 1
## 551 Unknown Unknown 0 239.99 1
## 552 Unknown Unknown 0 255.00 1
## 553 Unknown Unknown 0 259.00 1
## 554 Unknown Unknown 0 259.99 1
## 555 Unknown Unknown 0 260.00 1
## 556 Unknown Unknown 0 265.00 1
## 557 Unknown Unknown 0 275.00 1
## 558 Unknown Unknown 0 276.99 1
## 559 Unknown Unknown 0 279.00 1
## 560 Unknown Unknown 0 285.00 1
## 561 Unknown Unknown 0 289.00 1
## 562 Unknown Unknown 0 295.00 1
## 563 Unknown Unknown 0 309.98 1
## 564 Unknown Unknown 0 318.00 1
## 565 Unknown Unknown 0 325.00 1
## 566 Unknown Unknown 0 329.99 1
## 567 Unknown Unknown 0 334.99 1
## 568 Unknown Unknown 0 339.00 1
## 569 Unknown Unknown 0 340.00 1
## 570 Unknown Unknown 0 347.00 1
## 571 Unknown Unknown 0 349.99 1
## 572 Unknown Unknown 0 380.00 1
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## 1145 iPad mini iPad mini 0 149.59 1
## 1146 iPad mini iPad mini 0 149.95 1
## 1147 iPad mini iPad mini 0 149.99 1
## 1148 iPad mini iPad mini 0 159.95 1
## 1149 iPad mini iPad mini 0 160.57 1
## 1150 iPad mini iPad mini 0 168.00 1
## 1151 iPad mini iPad mini 0 170.00 1
## 1152 iPad mini iPad mini 0 171.95 1
## 1153 iPad mini iPad mini 0 176.27 1
## 1154 iPad mini iPad mini 0 178.99 1
## 1155 iPad mini iPad mini 0 179.00 1
## 1156 iPad mini iPad mini 0 179.96 1
## 1157 iPad mini iPad mini 0 180.00 1
## 1158 iPad mini iPad mini 0 181.00 1
## 1159 iPad mini iPad mini 0 184.99 1
## 1160 iPad mini iPad mini 0 185.00 1
## 1161 iPad mini iPad mini 0 185.49 1
## 1162 iPad mini iPad mini 0 187.89 1
## 1163 iPad mini iPad mini 0 188.88 1
## 1164 iPad mini iPad mini 0 190.00 1
## 1165 iPad mini iPad mini 0 194.29 1
## 1166 iPad mini iPad mini 0 195.00 1
## 1167 iPad mini iPad mini 0 198.00 1
## 1168 iPad mini iPad mini 0 199.97 1
## 1169 iPad mini iPad mini 0 205.00 1
## 1170 iPad mini iPad mini 0 208.00 1
## 1171 iPad mini iPad mini 0 208.99 1
## 1172 iPad mini iPad mini 0 209.00 1
## 1173 iPad mini iPad mini 0 209.85 1
## 1174 iPad mini iPad mini 0 209.99 1
## 1175 iPad mini iPad mini 0 211.50 1
## 1176 iPad mini iPad mini 0 212.99 1
## 1177 iPad mini iPad mini 0 214.98 1
## 1178 iPad mini iPad mini 0 215.99 1
## 1179 iPad mini iPad mini 0 219.00 1
## 1180 iPad mini iPad mini 0 220.00 1
## 1181 iPad mini iPad mini 0 227.88 1
## 1182 iPad mini iPad mini 0 235.00 1
## 1183 iPad mini iPad mini 0 239.00 1
## 1184 iPad mini iPad mini 0 240.00 1
## 1185 iPad mini iPad mini 0 241.88 1
## 1186 iPad mini iPad mini 0 244.97 1
## 1187 iPad mini iPad mini 0 249.95 1
## 1188 iPad mini iPad mini 0 252.88 1
## 1189 iPad mini iPad mini 0 255.00 1
## 1190 iPad mini iPad mini 0 258.88 1
## 1191 iPad mini iPad mini 0 259.00 1
## 1192 iPad mini iPad mini 0 260.00 1
## 1193 iPad mini iPad mini 0 265.00 1
## 1194 iPad mini iPad mini 0 265.99 1
## 1195 iPad mini iPad mini 0 271.00 1
## 1196 iPad mini iPad mini 0 279.00 1
## 1197 iPad mini iPad mini 0 279.50 1
## 1198 iPad mini iPad mini 0 279.99 1
## 1199 iPad mini iPad mini 0 289.00 1
## 1200 iPad mini iPad mini 0 289.99 1
## 1201 iPad mini iPad mini 0 295.00 1
## 1202 iPad mini iPad mini 0 298.00 1
## 1203 iPad mini iPad mini 0 299.95 1
## 1204 iPad mini iPad mini 0 310.00 1
## 1205 iPad mini iPad mini 0 315.00 1
## 1206 iPad mini iPad mini 0 320.00 1
## 1207 iPad mini iPad mini 0 334.95 1
## 1208 iPad mini iPad mini 0 339.99 1
## 1209 iPad mini iPad mini 0 348.60 1
## 1210 iPad mini iPad mini 0 349.99 1
## 1211 iPad mini iPad mini 0 351.00 1
## 1212 iPad mini iPad mini 0 358.87 1
## 1213 iPad mini iPad mini 0 370.00 1
## 1214 iPad mini iPad mini 0 375.00 1
## 1215 iPad mini iPad mini 0 379.99 1
## 1216 iPad mini iPad mini 0 385.00 1
## 1217 iPad mini iPad mini 0 387.45 1
## 1218 iPad mini iPad mini 0 388.30 1
## 1219 iPad mini iPad mini 0 397.75 1
## 1220 iPad mini iPad mini 0 398.99 1
## 1221 iPad mini iPad mini 0 399.99 1
## 1222 iPad mini iPad mini 0 429.99 1
## 1223 iPad mini iPad mini 0 475.00 1
## 1224 iPad mini iPad mini 0 499.99 1
## 1225 iPad mini iPad mini 0 720.12 1
## 1226 iPad mini iPad mini 0 999.00 1
## 1227 iPad mini iPad mini 1 9.99 1
## 1228 iPad mini iPad mini 1 49.99 1
## 1229 iPad mini iPad mini 1 100.00 1
## 1230 iPad mini iPad mini 1 149.00 1
## 1231 iPad mini iPad mini 1 169.99 1
## 1232 iPad mini iPad mini 1 249.99 1
## 1233 iPad mini iPad mini 1 429.00 1
## 1234 iPad mini iPad mini 2 99.99 1
## 1235 iPad mini 2 iPad mini 2 0 0.01 1
## 1236 iPad mini 2 iPad mini 2 0 10.00 1
## 1237 iPad mini 2 iPad mini 2 0 25.00 1
## 1238 iPad mini 2 iPad mini 2 0 49.99 1
## 1239 iPad mini 2 iPad mini 2 0 79.95 1
## 1240 iPad mini 2 iPad mini 2 0 99.97 1
## 1241 iPad mini 2 iPad mini 2 0 119.00 1
## 1242 iPad mini 2 iPad mini 2 0 129.99 1
## 1243 iPad mini 2 iPad mini 2 0 130.00 1
## 1244 iPad mini 2 iPad mini 2 0 145.00 1
## 1245 iPad mini 2 iPad mini 2 0 149.00 1
## 1246 iPad mini 2 iPad mini 2 0 149.95 1
## 1247 iPad mini 2 iPad mini 2 0 150.00 1
## 1248 iPad mini 2 iPad mini 2 0 155.00 1
## 1249 iPad mini 2 iPad mini 2 0 160.00 1
## 1250 iPad mini 2 iPad mini 2 0 185.00 1
## 1251 iPad mini 2 iPad mini 2 0 199.00 1
## 1252 iPad mini 2 iPad mini 2 0 209.98 1
## 1253 iPad mini 2 iPad mini 2 0 210.00 1
## 1254 iPad mini 2 iPad mini 2 0 215.00 1
## 1255 iPad mini 2 iPad mini 2 0 217.00 1
## 1256 iPad mini 2 iPad mini 2 0 222.72 1
## 1257 iPad mini 2 iPad mini 2 0 223.00 1
## 1258 iPad mini 2 iPad mini 2 0 229.00 1
## 1259 iPad mini 2 iPad mini 2 0 237.00 1
## 1260 iPad mini 2 iPad mini 2 0 239.00 1
## 1261 iPad mini 2 iPad mini 2 0 239.99 1
## 1262 iPad mini 2 iPad mini 2 0 245.00 1
## 1263 iPad mini 2 iPad mini 2 0 248.18 1
## 1264 iPad mini 2 iPad mini 2 0 249.00 1
## 1265 iPad mini 2 iPad mini 2 0 259.95 1
## 1266 iPad mini 2 iPad mini 2 0 260.00 1
## 1267 iPad mini 2 iPad mini 2 0 264.99 1
## 1268 iPad mini 2 iPad mini 2 0 279.99 1
## 1269 iPad mini 2 iPad mini 2 0 289.95 1
## 1270 iPad mini 2 iPad mini 2 0 295.00 1
## 1271 iPad mini 2 iPad mini 2 0 299.99 1
## 1272 iPad mini 2 iPad mini 2 0 308.00 1
## 1273 iPad mini 2 iPad mini 2 0 310.00 1
## 1274 iPad mini 2 iPad mini 2 0 319.98 1
## 1275 iPad mini 2 iPad mini 2 0 319.99 1
## 1276 iPad mini 2 iPad mini 2 0 327.58 1
## 1277 iPad mini 2 iPad mini 2 0 339.00 1
## 1278 iPad mini 2 iPad mini 2 0 339.99 1
## 1279 iPad mini 2 iPad mini 2 0 376.00 1
## 1280 iPad mini 2 iPad mini 2 0 379.99 1
## 1281 iPad mini 2 iPad mini 2 0 380.00 1
## 1282 iPad mini 2 iPad mini 2 0 385.00 1
## 1283 iPad mini 2 iPad mini 2 0 387.00 1
## 1284 iPad mini 2 iPad mini 2 0 395.00 1
## 1285 iPad mini 2 iPad mini 2 0 400.00 1
## 1286 iPad mini 2 iPad mini 2 0 429.99 1
## 1287 iPad mini 2 iPad mini 2 0 430.00 1
## 1288 iPad mini 2 iPad mini 2 0 449.00 1
## 1289 iPad mini 2 iPad mini 2 0 450.00 1
## 1290 iPad mini 2 iPad mini 2 0 458.00 1
## 1291 iPad mini 2 iPad mini 2 0 460.00 1
## 1292 iPad mini 2 iPad mini 2 0 469.00 1
## 1293 iPad mini 2 iPad mini 2 0 500.00 1
## 1294 iPad mini 2 iPad mini 2 0 509.00 1
## 1295 iPad mini 2 iPad mini 2 0 550.00 1
## 1296 iPad mini 2 iPad mini 2 0 575.00 1
## 1297 iPad mini 2 iPad mini 2 0 595.00 1
## 1298 iPad mini 2 iPad mini 2 1 195.00 1
## 1299 iPad mini 2 iPad mini 2 1 201.99 1
## 1300 iPad mini 2 iPad mini 2 1 225.00 1
## 1301 iPad mini 2 iPad mini 2 1 238.80 1
## 1302 iPad mini 2 iPad mini 2 1 249.00 1
## 1303 iPad mini 2 iPad mini 2 1 300.00 1
## 1304 iPad mini 2 iPad mini 2 1 350.25 1
## 1305 iPad mini 3 iPad mini 3 0 0.45 1
## 1306 iPad mini 3 iPad mini 3 0 9.95 1
## 1307 iPad mini 3 iPad mini 3 0 25.00 1
## 1308 iPad mini 3 iPad mini 3 0 100.00 1
## 1309 iPad mini 3 iPad mini 3 0 149.00 1
## 1310 iPad mini 3 iPad mini 3 0 175.00 1
## 1311 iPad mini 3 iPad mini 3 0 197.97 1
## 1312 iPad mini 3 iPad mini 3 0 199.99 1
## 1313 iPad mini 3 iPad mini 3 0 249.00 1
## 1314 iPad mini 3 iPad mini 3 0 250.00 1
## 1315 iPad mini 3 iPad mini 3 0 290.00 1
## 1316 iPad mini 3 iPad mini 3 0 295.95 1
## 1317 iPad mini 3 iPad mini 3 0 299.00 1
## 1318 iPad mini 3 iPad mini 3 0 309.95 1
## 1319 iPad mini 3 iPad mini 3 0 329.00 1
## 1320 iPad mini 3 iPad mini 3 0 331.99 1
## 1321 iPad mini 3 iPad mini 3 0 332.50 1
## 1322 iPad mini 3 iPad mini 3 0 334.00 1
## 1323 iPad mini 3 iPad mini 3 0 335.00 1
## 1324 iPad mini 3 iPad mini 3 0 339.50 1
## 1325 iPad mini 3 iPad mini 3 0 339.98 1
## 1326 iPad mini 3 iPad mini 3 0 340.00 1
## 1327 iPad mini 3 iPad mini 3 0 349.95 1
## 1328 iPad mini 3 iPad mini 3 0 349.99 1
## 1329 iPad mini 3 iPad mini 3 0 359.00 1
## 1330 iPad mini 3 iPad mini 3 0 359.99 1
## 1331 iPad mini 3 iPad mini 3 0 370.00 1
## 1332 iPad mini 3 iPad mini 3 0 379.95 1
## 1333 iPad mini 3 iPad mini 3 0 379.99 1
## 1334 iPad mini 3 iPad mini 3 0 380.00 1
## 1335 iPad mini 3 iPad mini 3 0 385.00 1
## 1336 iPad mini 3 iPad mini 3 0 394.99 1
## 1337 iPad mini 3 iPad mini 3 0 399.00 1
## 1338 iPad mini 3 iPad mini 3 0 419.95 1
## 1339 iPad mini 3 iPad mini 3 0 425.00 1
## 1340 iPad mini 3 iPad mini 3 0 426.99 1
## 1341 iPad mini 3 iPad mini 3 0 439.99 1
## 1342 iPad mini 3 iPad mini 3 0 445.95 1
## 1343 iPad mini 3 iPad mini 3 0 449.95 1
## 1344 iPad mini 3 iPad mini 3 0 450.00 1
## 1345 iPad mini 3 iPad mini 3 0 459.99 1
## 1346 iPad mini 3 iPad mini 3 0 469.99 1
## 1347 iPad mini 3 iPad mini 3 0 475.00 1
## 1348 iPad mini 3 iPad mini 3 0 485.00 1
## 1349 iPad mini 3 iPad mini 3 0 510.00 1
## 1350 iPad mini 3 iPad mini 3 0 525.00 1
## 1351 iPad mini 3 iPad mini 3 0 529.99 1
## 1352 iPad mini 3 iPad mini 3 0 549.99 1
## 1353 iPad mini 3 iPad mini 3 0 550.00 1
## 1354 iPad mini 3 iPad mini 3 0 559.99 1
## 1355 iPad mini 3 iPad mini 3 0 569.00 1
## 1356 iPad mini 3 iPad mini 3 0 575.00 1
## 1357 iPad mini 3 iPad mini 3 0 579.99 1
## 1358 iPad mini 3 iPad mini 3 0 609.99 1
## 1359 iPad mini 3 iPad mini 3 0 614.99 1
## 1360 iPad mini 3 iPad mini 3 0 639.99 1
## 1361 iPad mini 3 iPad mini 3 0 650.00 1
## 1362 iPad mini 3 iPad mini 3 0 689.99 1
## 1363 iPad mini 3 iPad mini 3 0 799.99 1
## 1364 iPad mini 3 iPad mini 3 0 948.98 1
## 1365 iPad mini 3 iPad mini 3 1 400.00 1
## 1366 iPad mini 3 iPad mini 3 1 419.99 1
## 1367 iPad mini 3 iPad mini 3 1 460.00 1
## 1368 iPad mini 3 iPad mini 3 1 499.99 1
## 1369 iPad mini 3 iPad mini 3 1 599.99 1
## 1370 iPad mini Retina iPad mini Retina 0 160.00 1
## 1371 iPad mini Retina iPad mini Retina 0 235.00 1
## 1372 iPad mini Retina iPad mini Retina 0 250.00 1
## 1373 iPad mini Retina iPad mini Retina 0 299.00 1
## 1374 iPad mini Retina iPad mini Retina 0 339.00 1
## 1375 iPad mini Retina iPad mini Retina 0 350.00 1
## 1376 iPad mini Retina iPad mini Retina 0 420.00 1
## 1377 iPad mini Retina iPad mini Retina 1 303.67 1print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
(glb_allobs_df$D.P.mini > 0),
c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])## UniqueID prdline.my sold .grpid color condition
## 1172 11172 Unknown 0 8 Unknown Used
## 1803 11804 Unknown 1 <NA> White Seller refurbished
## 2223 12225 Unknown NA 8 Unknown Used
## 2472 12474 iPad mini NA <NA> Space Gray Used
## 2623 12625 Unknown NA <NA> White For parts or not working
## cellular carrier storage
## 1172 Unknown Unknown 16
## 1803 1 AT&T Unknown
## 2223 Unknown Unknown 16
## 2472 0 None Unknown
## 2623 Unknown Unknown Unknown
## descr.my
## 1172 IPAD mini . not sure of what generation it can be. selling as is or best offer. had a crack but
## 1803 30 Day Warranty. Refurbished iPad Mini with signs of normal wear including possible scratching on
## 2223 IPAD mini . not sure of what generation it can be. selling as is or best offer. had a crack but
## 2472 here we have spacegray apple ipad mini a1432 no charger works great has small nicks nothing major
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may beglb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
"prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
glb_rsp_var)))## prdline.my productline D.P.air startprice .n
## 1 iPad 2 iPad 2 0 0.99 38
## 2 iPad mini iPad mini 0 0.99 34
## 3 iPad 1 iPad 1 0 0.99 26
## 4 Unknown Unknown 0 0.99 25
## 5 iPad 1 iPad 1 0 50.00 22
## 6 iPad mini iPad mini 0 150.00 20
## 7 iPad Air iPad Air 0 0.99 17
## 8 iPad 2 iPad 2 0 150.00 16
## 9 iPad 4 iPad 4 0 0.99 15
## 10 iPad mini iPad mini 0 100.00 15
## 11 iPad 2 iPad 2 0 100.00 13
## 12 iPad Air 2 iPad Air 2 0 0.99 13
## 13 iPad mini 2 iPad mini 2 0 0.99 13
## 14 iPad 1 iPad 1 0 80.00 12
## 15 iPad 3 iPad 3 0 0.99 12
## 16 iPad 3 iPad 3 0 200.00 12
## 17 iPad 1 iPad 1 0 90.00 11
## 18 iPad 2 iPad 2 0 175.00 11
## 19 iPad mini iPad mini 0 99.99 11
## 20 Unknown Unknown 0 150.00 10
## 21 iPad 1 iPad 1 0 75.00 10
## 22 iPad 1 iPad 1 0 100.00 10
## 23 iPad 2 iPad 2 0 0.01 10
## 24 iPad 3 iPad 3 0 250.00 10
## 25 iPad mini iPad mini 0 50.00 10
## 26 Unknown Unknown 0 100.00 9
## 27 iPad 2 iPad 2 0 99.99 9
## 28 iPad 2 iPad 2 0 149.99 9
## 29 iPad 2 iPad 2 0 199.99 9
## 30 iPad Air iPad Air 0 300.00 9
## 31 iPad mini iPad mini 0 199.99 9
## 32 Unknown Unknown 0 300.00 8
## 33 iPad 1 iPad 1 0 95.00 8
## 34 iPad 2 iPad 2 0 99.00 8
## 35 iPad 2 iPad 2 0 125.00 8
## 36 iPad 2 iPad 2 0 200.00 8
## 37 iPad 4 iPad 4 0 249.99 8
## 38 iPad Air 2 iPad Air 2 0 550.00 8
## 39 iPad mini iPad mini 0 200.00 8
## 40 iPad mini 2 iPad mini 2 0 350.00 8
## 41 Unknown Unknown 0 50.00 7
## 42 iPad 1 iPad 1 0 70.00 7
## 43 iPad 2 iPad 2 0 9.99 7
## 44 iPad 2 iPad 2 0 75.00 7
## 45 iPad 2 iPad 2 0 180.00 7
## 46 iPad 4 iPad 4 0 199.99 7
## 47 iPad mini iPad mini 0 99.00 7
## 48 iPad mini 3 iPad mini 3 0 0.99 7
## 49 iPad 1 iPad 1 0 1.00 6
## 50 iPad 2 iPad 2 0 50.00 6
## 51 iPad 2 iPad 2 0 160.00 6
## 52 iPad 4 iPad 4 0 100.00 6
## 53 iPad 4 iPad 4 0 150.00 6
## 54 iPad Air iPad Air 0 1.00 6
## 55 iPad Air iPad Air 0 200.00 6
## 56 iPad Air iPad Air 0 400.00 6
## 57 iPad Air 2 iPad Air 2 0 450.00 6
## 58 iPad mini iPad mini 0 75.00 6
## 59 iPad mini iPad mini 0 89.99 6
## 60 iPad mini iPad mini 0 159.99 6
## 61 iPad mini iPad mini 0 175.00 6
## 62 iPad mini iPad mini 0 199.00 6
## 63 iPad mini 2 iPad mini 2 0 225.00 6
## 64 iPad 1 iPad 1 0 29.99 5
## 65 iPad 1 iPad 1 0 55.00 5
## 66 iPad 1 iPad 1 0 79.99 5
## 67 iPad 1 iPad 1 0 99.00 5
## 68 iPad 2 iPad 2 0 80.00 5
## 69 iPad 2 iPad 2 0 165.00 5
## 70 iPad 2 iPad 2 0 179.00 5
## 71 iPad 3 iPad 3 0 99.00 5
## 72 iPad 3 iPad 3 0 150.00 5
## 73 iPad 3 iPad 3 0 220.00 5
## 74 iPad 3 iPad 3 0 225.00 5
## 75 iPad 3 iPad 3 0 300.00 5
## 76 iPad 4 iPad 4 0 250.00 5
## 77 iPad 4 iPad 4 0 279.99 5
## 78 iPad 4 iPad 4 0 400.00 5
## 79 iPad Air iPad Air 0 100.00 5
## 80 iPad Air iPad Air 0 250.00 5
## 81 iPad Air iPad Air 0 350.00 5
## 82 iPad Air iPad Air 0 389.99 5
## 83 iPad Air 2 iPad Air 2 0 499.99 5
## 84 iPad mini iPad mini 0 1.00 5
## 85 iPad mini iPad mini 0 250.00 5
## 86 iPad mini iPad mini 0 350.00 5
## 87 iPad mini 2 iPad mini 2 0 200.00 5
## 88 iPad mini 3 iPad mini 3 0 499.99 5
## 89 iPad mini 3 iPad mini 3 0 599.99 5
## 90 Unknown Unknown 0 25.00 4
## 91 Unknown Unknown 0 149.99 4
## 92 Unknown Unknown 0 250.00 4
## 93 iPad 1 iPad 1 0 40.00 4
## 94 iPad 1 iPad 1 0 49.99 4
## 95 iPad 1 iPad 1 0 79.00 4
## 96 iPad 1 iPad 1 0 105.00 4
## 97 iPad 1 iPad 1 0 110.00 4
## 98 iPad 2 iPad 2 0 1.00 4
## 99 iPad 2 iPad 2 0 40.00 4
## 100 iPad 2 iPad 2 0 49.99 4
## 101 iPad 2 iPad 2 0 130.00 4
## 102 iPad 2 iPad 2 0 140.00 4
## 103 iPad 2 iPad 2 0 155.00 4
## 104 iPad 2 iPad 2 0 164.99 4
## 105 iPad 2 iPad 2 0 174.99 4
## 106 iPad 2 iPad 2 0 179.99 4
## 107 iPad 2 iPad 2 0 189.99 4
## 108 iPad 2 iPad 2 0 250.00 4
## 109 iPad 3 iPad 3 0 100.00 4
## 110 iPad 3 iPad 3 0 149.99 4
## 111 iPad 3 iPad 3 0 175.00 4
## 112 iPad 3 iPad 3 0 199.99 4
## 113 iPad 3 iPad 3 0 219.99 4
## 114 iPad 3 iPad 3 0 249.99 4
## 115 iPad 3 iPad 3 0 275.00 4
## 116 iPad 4 iPad 4 0 0.01 4
## 117 iPad 4 iPad 4 0 99.99 4
## 118 iPad 4 iPad 4 0 200.00 4
## 119 iPad 4 iPad 4 0 299.00 4
## 120 iPad Air iPad Air 0 279.99 4
## 121 iPad Air iPad Air 0 325.00 4
## 122 iPad Air iPad Air 0 329.99 4
## 123 iPad Air iPad Air 0 500.00 4
## 124 iPad Air 2 iPad Air 2 0 250.00 4
## 125 iPad Air 2 iPad Air 2 0 350.00 4
## 126 iPad Air 2 iPad Air 2 0 399.00 4
## 127 iPad Air 2 iPad Air 2 0 399.99 4
## 128 iPad Air 2 iPad Air 2 0 400.00 4
## 129 iPad Air 2 iPad Air 2 0 500.00 4
## 130 iPad Air 2 iPad Air 2 0 549.99 4
## 131 iPad mini iPad mini 0 119.99 4
## 132 iPad mini iPad mini 0 130.00 4
## 133 iPad mini iPad mini 0 149.00 4
## 134 iPad mini iPad mini 0 249.99 4
## 135 iPad mini iPad mini 0 275.00 4
## 136 iPad mini iPad mini 0 300.00 4
## 137 iPad mini 2 iPad mini 2 0 175.00 4
## 138 iPad mini 2 iPad mini 2 0 250.00 4
## 139 iPad mini 2 iPad mini 2 0 300.00 4
## 140 iPad mini 3 iPad mini 3 0 325.00 4
## 141 iPad mini 3 iPad mini 3 0 400.00 4
## 142 Unknown Unknown 0 15.00 3
## 143 Unknown Unknown 0 40.00 3
## 144 Unknown Unknown 0 75.00 3
## 145 Unknown Unknown 0 99.00 3
## 146 Unknown Unknown 0 120.00 3
## 147 Unknown Unknown 0 199.00 3
## 148 Unknown Unknown 0 199.99 3
## 149 Unknown Unknown 0 200.00 3
## 150 Unknown Unknown 0 249.00 3
## 151 Unknown Unknown 0 249.99 3
## 152 Unknown Unknown 0 299.99 3
## 153 Unknown Unknown 0 319.00 3
## 154 Unknown Unknown 0 350.00 3
## 155 iPad 1 iPad 1 0 0.01 3
## 156 iPad 1 iPad 1 0 19.99 3
## 157 iPad 1 iPad 1 0 20.00 3
## 158 iPad 1 iPad 1 0 25.00 3
## 159 iPad 1 iPad 1 0 30.00 3
## 160 iPad 1 iPad 1 0 36.95 3
## 161 iPad 1 iPad 1 0 65.00 3
## 162 iPad 1 iPad 1 0 84.99 3
## 163 iPad 1 iPad 1 0 85.00 3
## 164 iPad 1 iPad 1 0 89.00 3
## 165 iPad 1 iPad 1 0 99.99 3
## 166 iPad 1 iPad 1 0 119.99 3
## 167 iPad 1 iPad 1 0 150.00 3
## 168 iPad 1 iPad 1 0 180.00 3
## 169 iPad 2 iPad 2 0 30.00 3
## 170 iPad 2 iPad 2 0 70.00 3
## 171 iPad 2 iPad 2 0 85.00 3
## 172 iPad 2 iPad 2 0 89.99 3
## 173 iPad 2 iPad 2 0 90.00 3
## 174 iPad 2 iPad 2 0 120.00 3
## 175 iPad 2 iPad 2 0 129.95 3
## 176 iPad 2 iPad 2 0 129.99 3
## 177 iPad 2 iPad 2 0 139.00 3
## 178 iPad 2 iPad 2 0 149.00 3
## 179 iPad 2 iPad 2 0 149.95 3
## 180 iPad 2 iPad 2 0 154.00 3
## 181 iPad 2 iPad 2 0 159.99 3
## 182 iPad 2 iPad 2 0 169.00 3
## 183 iPad 2 iPad 2 0 249.97 3
## 184 iPad 2 iPad 2 0 275.00 3
## 185 iPad 2 iPad 2 0 300.00 3
## 186 iPad 3 iPad 3 0 1.00 3
## 187 iPad 3 iPad 3 0 9.99 3
## 188 iPad 3 iPad 3 0 10.00 3
## 189 iPad 3 iPad 3 0 99.99 3
## 190 iPad 3 iPad 3 0 128.00 3
## 191 iPad 3 iPad 3 0 185.00 3
## 192 iPad 3 iPad 3 0 187.50 3
## 193 iPad 3 iPad 3 0 199.00 3
## 194 iPad 4 iPad 4 0 50.00 3
## 195 iPad 4 iPad 4 0 225.00 3
## 196 iPad 4 iPad 4 0 259.99 3
## 197 iPad 4 iPad 4 0 275.00 3
## 198 iPad 4 iPad 4 0 280.00 3
## 199 iPad 4 iPad 4 0 300.00 3
## 200 iPad 4 iPad 4 0 320.00 3
## 201 iPad Air iPad Air 0 90.00 3
## 202 iPad Air iPad Air 0 199.99 3
## 203 iPad Air iPad Air 0 229.00 3
## 204 iPad Air iPad Air 0 299.99 3
## 205 iPad Air iPad Air 0 320.00 3
## 206 iPad Air iPad Air 0 379.00 3
## 207 iPad Air iPad Air 0 415.00 3
## 208 iPad Air 2 iPad Air 2 0 1.00 3
## 209 iPad Air 2 iPad Air 2 0 50.00 3
## 210 iPad Air 2 iPad Air 2 0 199.99 3
## 211 iPad Air 2 iPad Air 2 0 425.00 3
## 212 iPad Air 2 iPad Air 2 0 439.99 3
## 213 iPad Air 2 iPad Air 2 0 480.00 3
## 214 iPad Air 2 iPad Air 2 0 499.00 3
## 215 iPad Air 2 iPad Air 2 0 525.00 3
## 216 iPad Air 2 iPad Air 2 0 560.00 3
## 217 iPad mini iPad mini 0 0.01 3
## 218 iPad mini iPad mini 0 20.00 3
## 219 iPad mini iPad mini 0 25.00 3
## 220 iPad mini iPad mini 0 45.00 3
## 221 iPad mini iPad mini 0 60.00 3
## 222 iPad mini iPad mini 0 125.00 3
## 223 iPad mini iPad mini 0 160.00 3
## 224 iPad mini iPad mini 0 179.00 3
## 225 iPad mini iPad mini 0 179.99 3
## 226 iPad mini iPad mini 0 189.99 3
## 227 iPad mini iPad mini 0 210.00 3
## 228 iPad mini iPad mini 0 259.99 3
## 229 iPad mini iPad mini 0 290.00 3
## 230 iPad mini iPad mini 0 400.00 3
## 231 iPad mini 2 iPad mini 2 0 100.00 3
## 232 iPad mini 2 iPad mini 2 0 120.00 3
## 233 iPad mini 2 iPad mini 2 0 180.00 3
## 234 iPad mini 2 iPad mini 2 0 285.00 3
## 235 iPad mini 2 iPad mini 2 0 375.00 3
## 236 iPad mini 3 iPad mini 3 0 99.00 3
## 237 iPad mini 3 iPad mini 3 0 300.00 3
## 238 iPad mini 3 iPad mini 3 0 329.99 3
## 239 iPad mini 3 iPad mini 3 0 350.00 3
## 240 iPad mini 3 iPad mini 3 0 399.99 3
## 241 iPad mini 3 iPad mini 3 0 449.99 3
## 242 iPad mini 3 iPad mini 3 0 729.99 3
## 243 Unknown Unknown 0 5.00 2
## 244 Unknown Unknown 0 9.99 2
## 245 Unknown Unknown 0 19.99 2
## 246 Unknown Unknown 0 20.00 2
## 247 Unknown Unknown 0 39.99 2
## 248 Unknown Unknown 0 70.00 2
## 249 Unknown Unknown 0 79.95 2
## 250 Unknown Unknown 0 80.00 2
## 251 Unknown Unknown 0 99.99 2
## 252 Unknown Unknown 0 108.00 2
## 253 Unknown Unknown 0 159.99 2
## 254 Unknown Unknown 0 165.00 2
## 255 Unknown Unknown 0 169.99 2
## 256 Unknown Unknown 0 175.00 2
## 257 Unknown Unknown 0 185.00 2
## 258 Unknown Unknown 0 280.00 2
## 259 Unknown Unknown 0 319.99 2
## 260 Unknown Unknown 0 375.00 2
## 261 Unknown Unknown 0 399.00 2
## 262 Unknown Unknown 0 450.00 2
## 263 Unknown Unknown 0 500.00 2
## 264 Unknown Unknown 0 599.99 2
## 265 Unknown Unknown 0 700.00 2
## 266 iPad 1 iPad 1 0 9.50 2
## 267 iPad 1 iPad 1 0 9.99 2
## 268 iPad 1 iPad 1 0 10.00 2
## 269 iPad 1 iPad 1 0 14.99 2
## 270 iPad 1 iPad 1 0 15.00 2
## 271 iPad 1 iPad 1 0 45.00 2
## 272 iPad 1 iPad 1 0 58.00 2
## 273 iPad 1 iPad 1 0 60.00 2
## 274 iPad 1 iPad 1 0 62.00 2
## 275 iPad 1 iPad 1 0 69.00 2
## 276 iPad 1 iPad 1 0 69.99 2
## 277 iPad 1 iPad 1 0 89.95 2
## 278 iPad 1 iPad 1 0 92.14 2
## 279 iPad 1 iPad 1 0 101.00 2
## 280 iPad 1 iPad 1 0 104.99 2
## 281 iPad 1 iPad 1 0 115.00 2
## 282 iPad 1 iPad 1 0 124.95 2
## 283 iPad 1 iPad 1 0 125.00 2
## 284 iPad 1 iPad 1 0 129.99 2
## 285 iPad 1 iPad 1 0 165.00 2
## 286 iPad 1 iPad 1 0 175.00 2
## 287 iPad 1 iPad 1 0 250.00 2
## 288 iPad 1 iPad 1 0 279.95 2
## 289 iPad 2 iPad 2 0 0.10 2
## 290 iPad 2 iPad 2 0 15.00 2
## 291 iPad 2 iPad 2 0 19.95 2
## 292 iPad 2 iPad 2 0 59.99 2
## 293 iPad 2 iPad 2 0 65.00 2
## 294 iPad 2 iPad 2 0 69.99 2
## 295 iPad 2 iPad 2 0 74.99 2
## 296 iPad 2 iPad 2 0 89.00 2
## 297 iPad 2 iPad 2 0 95.00 2
## 298 iPad 2 iPad 2 0 119.99 2
## 299 iPad 2 iPad 2 0 128.00 2
## 300 iPad 2 iPad 2 0 135.00 2
## 301 iPad 2 iPad 2 0 144.99 2
## 302 iPad 2 iPad 2 0 145.00 2
## 303 iPad 2 iPad 2 0 149.97 2
## 304 iPad 2 iPad 2 0 150.99 2
## 305 iPad 2 iPad 2 0 162.00 2
## 306 iPad 2 iPad 2 0 169.99 2
## 307 iPad 2 iPad 2 0 170.00 2
## 308 iPad 2 iPad 2 0 172.00 2
## 309 iPad 2 iPad 2 0 179.95 2
## 310 iPad 2 iPad 2 0 204.00 2
## 311 iPad 2 iPad 2 0 220.00 2
## 312 iPad 2 iPad 2 0 350.00 2
## 313 iPad 3 iPad 3 0 0.01 2
## 314 iPad 3 iPad 3 0 25.00 2
## 315 iPad 3 iPad 3 0 49.99 2
## 316 iPad 3 iPad 3 0 89.99 2
## 317 iPad 3 iPad 3 0 99.95 2
## 318 iPad 3 iPad 3 0 125.00 2
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## 934 iPad Air iPad Air 0 189.99 1
## 935 iPad Air iPad Air 0 199.00 1
## 936 iPad Air iPad Air 0 225.00 1
## 937 iPad Air iPad Air 0 240.00 1
## 938 iPad Air iPad Air 0 242.00 1
## 939 iPad Air iPad Air 0 249.00 1
## 940 iPad Air iPad Air 0 249.99 1
## 941 iPad Air iPad Air 0 255.00 1
## 942 iPad Air iPad Air 0 256.24 1
## 943 iPad Air iPad Air 0 257.83 1
## 944 iPad Air iPad Air 0 259.99 1
## 945 iPad Air iPad Air 0 266.05 1
## 946 iPad Air iPad Air 0 269.85 1
## 947 iPad Air iPad Air 0 270.99 1
## 948 iPad Air iPad Air 0 274.00 1
## 949 iPad Air iPad Air 0 274.99 1
## 950 iPad Air iPad Air 0 275.00 1
## 951 iPad Air iPad Air 0 279.00 1
## 952 iPad Air iPad Air 0 288.00 1
## 953 iPad Air iPad Air 0 289.79 1
## 954 iPad Air iPad Air 0 292.50 1
## 955 iPad Air iPad Air 0 294.99 1
## 956 iPad Air iPad Air 0 299.98 1
## 957 iPad Air iPad Air 0 310.00 1
## 958 iPad Air iPad Air 0 319.85 1
## 959 iPad Air iPad Air 0 322.99 1
## 960 iPad Air iPad Air 0 334.99 1
## 961 iPad Air iPad Air 0 339.99 1
## 962 iPad Air iPad Air 0 344.95 1
## 963 iPad Air iPad Air 0 346.00 1
## 964 iPad Air iPad Air 0 347.24 1
## 965 iPad Air iPad Air 0 349.95 1
## 966 iPad Air iPad Air 0 358.24 1
## 967 iPad Air iPad Air 0 359.99 1
## 968 iPad Air iPad Air 0 360.00 1
## 969 iPad Air iPad Air 0 360.24 1
## 970 iPad Air iPad Air 0 370.00 1
## 971 iPad Air iPad Air 0 374.95 1
## 972 iPad Air iPad Air 0 374.99 1
## 973 iPad Air iPad Air 0 375.99 1
## 974 iPad Air iPad Air 0 380.00 1
## 975 iPad Air iPad Air 0 384.99 1
## 976 iPad Air iPad Air 0 388.99 1
## 977 iPad Air iPad Air 0 389.00 1
## 978 iPad Air iPad Air 0 399.95 1
## 979 iPad Air iPad Air 0 404.99 1
## 980 iPad Air iPad Air 0 408.00 1
## 981 iPad Air iPad Air 0 420.00 1
## 982 iPad Air iPad Air 0 424.95 1
## 983 iPad Air iPad Air 0 429.99 1
## 984 iPad Air iPad Air 0 430.00 1
## 985 iPad Air iPad Air 0 438.00 1
## 986 iPad Air iPad Air 0 439.00 1
## 987 iPad Air iPad Air 0 439.99 1
## 988 iPad Air iPad Air 0 443.09 1
## 989 iPad Air iPad Air 0 455.00 1
## 990 iPad Air iPad Air 0 462.89 1
## 991 iPad Air iPad Air 0 469.99 1
## 992 iPad Air iPad Air 0 495.49 1
## 993 iPad Air iPad Air 0 509.99 1
## 994 iPad Air iPad Air 0 517.89 1
## 995 iPad Air iPad Air 0 539.95 1
## 996 iPad Air iPad Air 0 549.99 1
## 997 iPad Air iPad Air 0 550.00 1
## 998 iPad Air iPad Air 0 558.17 1
## 999 iPad Air iPad Air 0 565.95 1
## 1000 iPad Air iPad Air 0 589.99 1
## 1001 iPad Air iPad Air 0 599.99 1
## 1002 iPad Air iPad Air 0 650.00 1
## 1003 iPad Air iPad Air 0 670.00 1
## 1004 iPad Air iPad Air 0 699.00 1
## 1005 iPad Air iPad Air 0 795.99 1
## 1006 iPad Air iPad Air 0 820.00 1
## 1007 iPad Air iPad Air 1 149.99 1
## 1008 iPad Air iPad Air 1 199.00 1
## 1009 iPad Air iPad Air 1 199.99 1
## 1010 iPad Air iPad Air 1 229.00 1
## 1011 iPad Air iPad Air 1 279.00 1
## 1012 iPad Air iPad Air 1 290.00 1
## 1013 iPad Air iPad Air 1 349.00 1
## 1014 iPad Air iPad Air 1 449.99 1
## 1015 iPad Air 2 iPad Air 2 0 0.01 1
## 1016 iPad Air 2 iPad Air 2 0 1.99 1
## 1017 iPad Air 2 iPad Air 2 0 9.00 1
## 1018 iPad Air 2 iPad Air 2 0 60.00 1
## 1019 iPad Air 2 iPad Air 2 0 99.95 1
## 1020 iPad Air 2 iPad Air 2 0 100.00 1
## 1021 iPad Air 2 iPad Air 2 0 139.00 1
## 1022 iPad Air 2 iPad Air 2 0 229.98 1
## 1023 iPad Air 2 iPad Air 2 0 295.00 1
## 1024 iPad Air 2 iPad Air 2 0 299.00 1
## 1025 iPad Air 2 iPad Air 2 0 299.99 1
## 1026 iPad Air 2 iPad Air 2 0 305.00 1
## 1027 iPad Air 2 iPad Air 2 0 310.00 1
## 1028 iPad Air 2 iPad Air 2 0 319.99 1
## 1029 iPad Air 2 iPad Air 2 0 320.00 1
## 1030 iPad Air 2 iPad Air 2 0 324.99 1
## 1031 iPad Air 2 iPad Air 2 0 339.00 1
## 1032 iPad Air 2 iPad Air 2 0 374.95 1
## 1033 iPad Air 2 iPad Air 2 0 375.00 1
## 1034 iPad Air 2 iPad Air 2 0 380.00 1
## 1035 iPad Air 2 iPad Air 2 0 389.99 1
## 1036 iPad Air 2 iPad Air 2 0 394.99 1
## 1037 iPad Air 2 iPad Air 2 0 395.00 1
## 1038 iPad Air 2 iPad Air 2 0 399.94 1
## 1039 iPad Air 2 iPad Air 2 0 399.95 1
## 1040 iPad Air 2 iPad Air 2 0 410.00 1
## 1041 iPad Air 2 iPad Air 2 0 424.55 1
## 1042 iPad Air 2 iPad Air 2 0 424.65 1
## 1043 iPad Air 2 iPad Air 2 0 424.99 1
## 1044 iPad Air 2 iPad Air 2 0 429.00 1
## 1045 iPad Air 2 iPad Air 2 0 429.95 1
## 1046 iPad Air 2 iPad Air 2 0 429.99 1
## 1047 iPad Air 2 iPad Air 2 0 430.00 1
## 1048 iPad Air 2 iPad Air 2 0 438.99 1
## 1049 iPad Air 2 iPad Air 2 0 439.98 1
## 1050 iPad Air 2 iPad Air 2 0 440.00 1
## 1051 iPad Air 2 iPad Air 2 0 444.99 1
## 1052 iPad Air 2 iPad Air 2 0 445.00 1
## 1053 iPad Air 2 iPad Air 2 0 454.00 1
## 1054 iPad Air 2 iPad Air 2 0 454.68 1
## 1055 iPad Air 2 iPad Air 2 0 459.00 1
## 1056 iPad Air 2 iPad Air 2 0 459.95 1
## 1057 iPad Air 2 iPad Air 2 0 459.99 1
## 1058 iPad Air 2 iPad Air 2 0 469.99 1
## 1059 iPad Air 2 iPad Air 2 0 485.00 1
## 1060 iPad Air 2 iPad Air 2 0 489.99 1
## 1061 iPad Air 2 iPad Air 2 0 490.00 1
## 1062 iPad Air 2 iPad Air 2 0 490.95 1
## 1063 iPad Air 2 iPad Air 2 0 495.99 1
## 1064 iPad Air 2 iPad Air 2 0 499.95 1
## 1065 iPad Air 2 iPad Air 2 0 509.00 1
## 1066 iPad Air 2 iPad Air 2 0 510.00 1
## 1067 iPad Air 2 iPad Air 2 0 514.95 1
## 1068 iPad Air 2 iPad Air 2 0 515.00 1
## 1069 iPad Air 2 iPad Air 2 0 520.00 1
## 1070 iPad Air 2 iPad Air 2 0 528.00 1
## 1071 iPad Air 2 iPad Air 2 0 529.00 1
## 1072 iPad Air 2 iPad Air 2 0 529.95 1
## 1073 iPad Air 2 iPad Air 2 0 529.99 1
## 1074 iPad Air 2 iPad Air 2 0 549.90 1
## 1075 iPad Air 2 iPad Air 2 0 549.95 1
## 1076 iPad Air 2 iPad Air 2 0 559.00 1
## 1077 iPad Air 2 iPad Air 2 0 579.99 1
## 1078 iPad Air 2 iPad Air 2 0 585.99 1
## 1079 iPad Air 2 iPad Air 2 0 589.00 1
## 1080 iPad Air 2 iPad Air 2 0 590.00 1
## 1081 iPad Air 2 iPad Air 2 0 595.00 1
## 1082 iPad Air 2 iPad Air 2 0 598.98 1
## 1083 iPad Air 2 iPad Air 2 0 600.00 1
## 1084 iPad Air 2 iPad Air 2 0 614.99 1
## 1085 iPad Air 2 iPad Air 2 0 615.99 1
## 1086 iPad Air 2 iPad Air 2 0 619.00 1
## 1087 iPad Air 2 iPad Air 2 0 624.99 1
## 1088 iPad Air 2 iPad Air 2 0 625.00 1
## 1089 iPad Air 2 iPad Air 2 0 629.00 1
## 1090 iPad Air 2 iPad Air 2 0 630.00 1
## 1091 iPad Air 2 iPad Air 2 0 634.99 1
## 1092 iPad Air 2 iPad Air 2 0 645.00 1
## 1093 iPad Air 2 iPad Air 2 0 645.99 1
## 1094 iPad Air 2 iPad Air 2 0 649.95 1
## 1095 iPad Air 2 iPad Air 2 0 649.99 1
## 1096 iPad Air 2 iPad Air 2 0 659.49 1
## 1097 iPad Air 2 iPad Air 2 0 660.00 1
## 1098 iPad Air 2 iPad Air 2 0 675.00 1
## 1099 iPad Air 2 iPad Air 2 0 679.95 1
## 1100 iPad Air 2 iPad Air 2 0 679.99 1
## 1101 iPad Air 2 iPad Air 2 0 680.00 1
## 1102 iPad Air 2 iPad Air 2 0 710.00 1
## 1103 iPad Air 2 iPad Air 2 0 730.00 1
## 1104 iPad Air 2 iPad Air 2 0 740.00 1
## 1105 iPad Air 2 iPad Air 2 0 749.99 1
## 1106 iPad Air 2 iPad Air 2 0 785.00 1
## 1107 iPad Air 2 iPad Air 2 0 789.00 1
## 1108 iPad Air 2 iPad Air 2 0 789.99 1
## 1109 iPad Air 2 iPad Air 2 0 795.00 1
## 1110 iPad Air 2 iPad Air 2 0 798.00 1
## 1111 iPad Air 2 iPad Air 2 0 799.00 1
## 1112 iPad Air 2 iPad Air 2 0 800.00 1
## 1113 iPad Air 2 iPad Air 2 0 829.99 1
## 1114 iPad Air 2 iPad Air 2 0 879.99 1
## 1115 iPad Air 2 iPad Air 2 0 899.99 1
## 1116 iPad Air 2 iPad Air 2 0 900.00 1
## 1117 iPad Air 2 iPad Air 2 0 939.00 1
## 1118 iPad Air 2 iPad Air 2 1 10.00 1
## 1119 iPad Air 2 iPad Air 2 1 59.00 1
## 1120 iPad Air 2 iPad Air 2 1 619.99 1
## 1121 iPad Air 2 iPad Air 2 1 800.00 1
## 1122 iPad Air 2 iPad Air 2 2 499.00 1
## 1123 iPad mini Unknown 0 190.00 1
## 1124 iPad mini Unknown 0 409.99 1
## 1125 iPad mini Unknown 0 999.99 1
## 1126 iPad mini iPad mini 0 0.98 1
## 1127 iPad mini iPad mini 0 10.99 1
## 1128 iPad mini iPad mini 0 19.50 1
## 1129 iPad mini iPad mini 0 19.99 1
## 1130 iPad mini iPad mini 0 29.99 1
## 1131 iPad mini iPad mini 0 40.00 1
## 1132 iPad mini iPad mini 0 42.00 1
## 1133 iPad mini iPad mini 0 49.95 1
## 1134 iPad mini iPad mini 0 49.99 1
## 1135 iPad mini iPad mini 0 59.99 1
## 1136 iPad mini iPad mini 0 62.00 1
## 1137 iPad mini iPad mini 0 74.95 1
## 1138 iPad mini iPad mini 0 74.99 1
## 1139 iPad mini iPad mini 0 79.00 1
## 1140 iPad mini iPad mini 0 79.99 1
## 1141 iPad mini iPad mini 0 84.99 1
## 1142 iPad mini iPad mini 0 89.00 1
## 1143 iPad mini iPad mini 0 109.00 1
## 1144 iPad mini iPad mini 0 109.99 1
## 1145 iPad mini iPad mini 0 110.00 1
## 1146 iPad mini iPad mini 0 112.00 1
## 1147 iPad mini iPad mini 0 113.00 1
## 1148 iPad mini iPad mini 0 118.00 1
## 1149 iPad mini iPad mini 0 119.98 1
## 1150 iPad mini iPad mini 0 129.00 1
## 1151 iPad mini iPad mini 0 129.95 1
## 1152 iPad mini iPad mini 0 129.99 1
## 1153 iPad mini iPad mini 0 135.00 1
## 1154 iPad mini iPad mini 0 139.00 1
## 1155 iPad mini iPad mini 0 140.00 1
## 1156 iPad mini iPad mini 0 144.99 1
## 1157 iPad mini iPad mini 0 145.00 1
## 1158 iPad mini iPad mini 0 149.59 1
## 1159 iPad mini iPad mini 0 149.95 1
## 1160 iPad mini iPad mini 0 149.99 1
## 1161 iPad mini iPad mini 0 159.95 1
## 1162 iPad mini iPad mini 0 160.57 1
## 1163 iPad mini iPad mini 0 168.00 1
## 1164 iPad mini iPad mini 0 169.99 1
## 1165 iPad mini iPad mini 0 170.00 1
## 1166 iPad mini iPad mini 0 171.95 1
## 1167 iPad mini iPad mini 0 176.27 1
## 1168 iPad mini iPad mini 0 178.99 1
## 1169 iPad mini iPad mini 0 179.96 1
## 1170 iPad mini iPad mini 0 180.00 1
## 1171 iPad mini iPad mini 0 181.00 1
## 1172 iPad mini iPad mini 0 184.99 1
## 1173 iPad mini iPad mini 0 185.00 1
## 1174 iPad mini iPad mini 0 185.49 1
## 1175 iPad mini iPad mini 0 187.89 1
## 1176 iPad mini iPad mini 0 188.88 1
## 1177 iPad mini iPad mini 0 190.00 1
## 1178 iPad mini iPad mini 0 194.29 1
## 1179 iPad mini iPad mini 0 195.00 1
## 1180 iPad mini iPad mini 0 198.00 1
## 1181 iPad mini iPad mini 0 199.97 1
## 1182 iPad mini iPad mini 0 205.00 1
## 1183 iPad mini iPad mini 0 208.00 1
## 1184 iPad mini iPad mini 0 208.99 1
## 1185 iPad mini iPad mini 0 209.00 1
## 1186 iPad mini iPad mini 0 209.85 1
## 1187 iPad mini iPad mini 0 209.99 1
## 1188 iPad mini iPad mini 0 211.50 1
## 1189 iPad mini iPad mini 0 212.99 1
## 1190 iPad mini iPad mini 0 214.98 1
## 1191 iPad mini iPad mini 0 215.99 1
## 1192 iPad mini iPad mini 0 219.00 1
## 1193 iPad mini iPad mini 0 220.00 1
## 1194 iPad mini iPad mini 0 227.88 1
## 1195 iPad mini iPad mini 0 235.00 1
## 1196 iPad mini iPad mini 0 239.00 1
## 1197 iPad mini iPad mini 0 240.00 1
## 1198 iPad mini iPad mini 0 241.88 1
## 1199 iPad mini iPad mini 0 244.97 1
## 1200 iPad mini iPad mini 0 249.95 1
## 1201 iPad mini iPad mini 0 252.88 1
## 1202 iPad mini iPad mini 0 255.00 1
## 1203 iPad mini iPad mini 0 258.88 1
## 1204 iPad mini iPad mini 0 259.00 1
## 1205 iPad mini iPad mini 0 260.00 1
## 1206 iPad mini iPad mini 0 265.00 1
## 1207 iPad mini iPad mini 0 265.99 1
## 1208 iPad mini iPad mini 0 271.00 1
## 1209 iPad mini iPad mini 0 279.00 1
## 1210 iPad mini iPad mini 0 279.50 1
## 1211 iPad mini iPad mini 0 279.99 1
## 1212 iPad mini iPad mini 0 289.00 1
## 1213 iPad mini iPad mini 0 289.99 1
## 1214 iPad mini iPad mini 0 295.00 1
## 1215 iPad mini iPad mini 0 298.00 1
## 1216 iPad mini iPad mini 0 299.95 1
## 1217 iPad mini iPad mini 0 310.00 1
## 1218 iPad mini iPad mini 0 315.00 1
## 1219 iPad mini iPad mini 0 320.00 1
## 1220 iPad mini iPad mini 0 334.95 1
## 1221 iPad mini iPad mini 0 339.99 1
## 1222 iPad mini iPad mini 0 348.60 1
## 1223 iPad mini iPad mini 0 349.99 1
## 1224 iPad mini iPad mini 0 351.00 1
## 1225 iPad mini iPad mini 0 358.87 1
## 1226 iPad mini iPad mini 0 370.00 1
## 1227 iPad mini iPad mini 0 375.00 1
## 1228 iPad mini iPad mini 0 379.99 1
## 1229 iPad mini iPad mini 0 385.00 1
## 1230 iPad mini iPad mini 0 387.45 1
## 1231 iPad mini iPad mini 0 388.30 1
## 1232 iPad mini iPad mini 0 397.75 1
## 1233 iPad mini iPad mini 0 398.99 1
## 1234 iPad mini iPad mini 0 399.99 1
## 1235 iPad mini iPad mini 0 429.00 1
## 1236 iPad mini iPad mini 0 429.99 1
## 1237 iPad mini iPad mini 0 475.00 1
## 1238 iPad mini iPad mini 0 499.99 1
## 1239 iPad mini iPad mini 0 720.12 1
## 1240 iPad mini iPad mini 0 999.00 1
## 1241 iPad mini 2 iPad mini 2 0 0.01 1
## 1242 iPad mini 2 iPad mini 2 0 10.00 1
## 1243 iPad mini 2 iPad mini 2 0 25.00 1
## 1244 iPad mini 2 iPad mini 2 0 49.99 1
## 1245 iPad mini 2 iPad mini 2 0 79.95 1
## 1246 iPad mini 2 iPad mini 2 0 99.97 1
## 1247 iPad mini 2 iPad mini 2 0 119.00 1
## 1248 iPad mini 2 iPad mini 2 0 129.99 1
## 1249 iPad mini 2 iPad mini 2 0 130.00 1
## 1250 iPad mini 2 iPad mini 2 0 145.00 1
## 1251 iPad mini 2 iPad mini 2 0 149.00 1
## 1252 iPad mini 2 iPad mini 2 0 149.95 1
## 1253 iPad mini 2 iPad mini 2 0 150.00 1
## 1254 iPad mini 2 iPad mini 2 0 155.00 1
## 1255 iPad mini 2 iPad mini 2 0 160.00 1
## 1256 iPad mini 2 iPad mini 2 0 185.00 1
## 1257 iPad mini 2 iPad mini 2 0 199.00 1
## 1258 iPad mini 2 iPad mini 2 0 201.99 1
## 1259 iPad mini 2 iPad mini 2 0 209.98 1
## 1260 iPad mini 2 iPad mini 2 0 210.00 1
## 1261 iPad mini 2 iPad mini 2 0 215.00 1
## 1262 iPad mini 2 iPad mini 2 0 217.00 1
## 1263 iPad mini 2 iPad mini 2 0 222.72 1
## 1264 iPad mini 2 iPad mini 2 0 223.00 1
## 1265 iPad mini 2 iPad mini 2 0 229.00 1
## 1266 iPad mini 2 iPad mini 2 0 237.00 1
## 1267 iPad mini 2 iPad mini 2 0 238.80 1
## 1268 iPad mini 2 iPad mini 2 0 239.00 1
## 1269 iPad mini 2 iPad mini 2 0 239.99 1
## 1270 iPad mini 2 iPad mini 2 0 245.00 1
## 1271 iPad mini 2 iPad mini 2 0 248.18 1
## 1272 iPad mini 2 iPad mini 2 0 259.95 1
## 1273 iPad mini 2 iPad mini 2 0 260.00 1
## 1274 iPad mini 2 iPad mini 2 0 264.99 1
## 1275 iPad mini 2 iPad mini 2 0 279.99 1
## 1276 iPad mini 2 iPad mini 2 0 289.95 1
## 1277 iPad mini 2 iPad mini 2 0 295.00 1
## 1278 iPad mini 2 iPad mini 2 0 299.99 1
## 1279 iPad mini 2 iPad mini 2 0 308.00 1
## 1280 iPad mini 2 iPad mini 2 0 310.00 1
## 1281 iPad mini 2 iPad mini 2 0 319.98 1
## 1282 iPad mini 2 iPad mini 2 0 319.99 1
## 1283 iPad mini 2 iPad mini 2 0 327.58 1
## 1284 iPad mini 2 iPad mini 2 0 339.00 1
## 1285 iPad mini 2 iPad mini 2 0 339.99 1
## 1286 iPad mini 2 iPad mini 2 0 350.25 1
## 1287 iPad mini 2 iPad mini 2 0 376.00 1
## 1288 iPad mini 2 iPad mini 2 0 379.99 1
## 1289 iPad mini 2 iPad mini 2 0 380.00 1
## 1290 iPad mini 2 iPad mini 2 0 385.00 1
## 1291 iPad mini 2 iPad mini 2 0 387.00 1
## 1292 iPad mini 2 iPad mini 2 0 395.00 1
## 1293 iPad mini 2 iPad mini 2 0 400.00 1
## 1294 iPad mini 2 iPad mini 2 0 429.99 1
## 1295 iPad mini 2 iPad mini 2 0 430.00 1
## 1296 iPad mini 2 iPad mini 2 0 449.00 1
## 1297 iPad mini 2 iPad mini 2 0 450.00 1
## 1298 iPad mini 2 iPad mini 2 0 458.00 1
## 1299 iPad mini 2 iPad mini 2 0 460.00 1
## 1300 iPad mini 2 iPad mini 2 0 469.00 1
## 1301 iPad mini 2 iPad mini 2 0 500.00 1
## 1302 iPad mini 2 iPad mini 2 0 509.00 1
## 1303 iPad mini 2 iPad mini 2 0 550.00 1
## 1304 iPad mini 2 iPad mini 2 0 575.00 1
## 1305 iPad mini 2 iPad mini 2 0 595.00 1
## 1306 iPad mini 2 iPad mini 2 1 195.00 1
## 1307 iPad mini 3 iPad mini 3 0 0.45 1
## 1308 iPad mini 3 iPad mini 3 0 9.95 1
## 1309 iPad mini 3 iPad mini 3 0 25.00 1
## 1310 iPad mini 3 iPad mini 3 0 100.00 1
## 1311 iPad mini 3 iPad mini 3 0 149.00 1
## 1312 iPad mini 3 iPad mini 3 0 175.00 1
## 1313 iPad mini 3 iPad mini 3 0 197.97 1
## 1314 iPad mini 3 iPad mini 3 0 199.99 1
## 1315 iPad mini 3 iPad mini 3 0 249.00 1
## 1316 iPad mini 3 iPad mini 3 0 250.00 1
## 1317 iPad mini 3 iPad mini 3 0 290.00 1
## 1318 iPad mini 3 iPad mini 3 0 295.95 1
## 1319 iPad mini 3 iPad mini 3 0 299.00 1
## 1320 iPad mini 3 iPad mini 3 0 309.95 1
## 1321 iPad mini 3 iPad mini 3 0 329.00 1
## 1322 iPad mini 3 iPad mini 3 0 331.99 1
## 1323 iPad mini 3 iPad mini 3 0 332.50 1
## 1324 iPad mini 3 iPad mini 3 0 334.00 1
## 1325 iPad mini 3 iPad mini 3 0 335.00 1
## 1326 iPad mini 3 iPad mini 3 0 339.50 1
## 1327 iPad mini 3 iPad mini 3 0 339.98 1
## 1328 iPad mini 3 iPad mini 3 0 340.00 1
## 1329 iPad mini 3 iPad mini 3 0 349.95 1
## 1330 iPad mini 3 iPad mini 3 0 349.99 1
## 1331 iPad mini 3 iPad mini 3 0 359.00 1
## 1332 iPad mini 3 iPad mini 3 0 359.99 1
## 1333 iPad mini 3 iPad mini 3 0 370.00 1
## 1334 iPad mini 3 iPad mini 3 0 379.95 1
## 1335 iPad mini 3 iPad mini 3 0 379.99 1
## 1336 iPad mini 3 iPad mini 3 0 380.00 1
## 1337 iPad mini 3 iPad mini 3 0 385.00 1
## 1338 iPad mini 3 iPad mini 3 0 394.99 1
## 1339 iPad mini 3 iPad mini 3 0 399.00 1
## 1340 iPad mini 3 iPad mini 3 0 419.95 1
## 1341 iPad mini 3 iPad mini 3 0 419.99 1
## 1342 iPad mini 3 iPad mini 3 0 425.00 1
## 1343 iPad mini 3 iPad mini 3 0 426.99 1
## 1344 iPad mini 3 iPad mini 3 0 439.99 1
## 1345 iPad mini 3 iPad mini 3 0 445.95 1
## 1346 iPad mini 3 iPad mini 3 0 449.95 1
## 1347 iPad mini 3 iPad mini 3 0 450.00 1
## 1348 iPad mini 3 iPad mini 3 0 459.99 1
## 1349 iPad mini 3 iPad mini 3 0 460.00 1
## 1350 iPad mini 3 iPad mini 3 0 469.99 1
## 1351 iPad mini 3 iPad mini 3 0 475.00 1
## 1352 iPad mini 3 iPad mini 3 0 485.00 1
## 1353 iPad mini 3 iPad mini 3 0 510.00 1
## 1354 iPad mini 3 iPad mini 3 0 525.00 1
## 1355 iPad mini 3 iPad mini 3 0 529.99 1
## 1356 iPad mini 3 iPad mini 3 0 549.99 1
## 1357 iPad mini 3 iPad mini 3 0 550.00 1
## 1358 iPad mini 3 iPad mini 3 0 559.99 1
## 1359 iPad mini 3 iPad mini 3 0 569.00 1
## 1360 iPad mini 3 iPad mini 3 0 575.00 1
## 1361 iPad mini 3 iPad mini 3 0 579.99 1
## 1362 iPad mini 3 iPad mini 3 0 609.99 1
## 1363 iPad mini 3 iPad mini 3 0 614.99 1
## 1364 iPad mini 3 iPad mini 3 0 639.99 1
## 1365 iPad mini 3 iPad mini 3 0 650.00 1
## 1366 iPad mini 3 iPad mini 3 0 689.99 1
## 1367 iPad mini 3 iPad mini 3 0 799.99 1
## 1368 iPad mini 3 iPad mini 3 0 948.98 1
## 1369 iPad mini Retina iPad mini Retina 0 160.00 1
## 1370 iPad mini Retina iPad mini Retina 0 235.00 1
## 1371 iPad mini Retina iPad mini Retina 0 250.00 1
## 1372 iPad mini Retina iPad mini Retina 0 299.00 1
## 1373 iPad mini Retina iPad mini Retina 0 303.67 1
## 1374 iPad mini Retina iPad mini Retina 0 339.00 1
## 1375 iPad mini Retina iPad mini Retina 0 350.00 1
## 1376 iPad mini Retina iPad mini Retina 0 420.00 1print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
(glb_allobs_df$D.P.air > 0),
c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])## UniqueID prdline.my sold .grpid color condition cellular carrier
## 946 10946 Unknown 0 <NA> Unknown Used Unknown Unknown
## 1360 11361 iPad Air 1 <NA> White Used 0 None
## 2433 12435 Unknown NA <NA> Space Gray Used Unknown Unknown
## storage
## 946 Unknown
## 1360 32
## 2433 128
## descr.my
## 946 Gently used apple iPad Air, no scratches on screen and almost no visible wear on back of item. No
## 1360 APPLE iPAD AIR 32GB WHITE MD789LL/ B WHITE. This item is Previously Lightly Used, in Good Condition.
## 2433 ***128gb*** black/ spacegray iPad Air excellent used condition(no scratches, dents, or blemishes)#glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
"prdline.my"] <- "iPad Air"
print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
c(glb_id_var, "sold",
"prdline.my", "color", "condition", "cellular", "carrier", "storage", "descr.my")])## UniqueID sold prdline.my color condition cellular
## 1766 11767 0 Unknown Unknown For parts or not working Unknown
## 1810 11811 0 Unknown Black Seller refurbished 0
## 2154 12156 NA Unknown Black Used 0
## carrier storage
## 1766 Unknown Unknown
## 1810 None Unknown
## 2154 None 32
## descr.my
## 1766 Ipad 2 32gb Housing. Some scratches and small dents, but overall good condition.
## 1810 30 Day Warranty. Refurbished iPad 2 with scratching on screen and wear on back plate. Comes with
## 2154 Original IPAD 1st generation - used one owner (myself)Good shape as pictured. Fully functional asglb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 314
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 314
## 6 iPadmini 2+ 299
## 7 Unknown 285#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"),
c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))## color D.P.black D.P.gold D.P.spacegray D.P.white .n
## 1 Unknown 0 0 0 0 1017
## 2 Unknown 0 0 0 1 4
## 3 Unknown 1 0 0 0 4
## 4 Unknown 0 0 1 0 1
## 5 Unknown 1 0 0 1 1print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0),
c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
"cellular", "carrier", "storage", "descr.my")])## UniqueID color D.P.black sold prdline.my condition cellular carrier
## 631 10631 Unknown 1 1 iPad 2 Used 1 AT&T
## 683 10683 Unknown 1 0 iPad 2 Used 0 None
## 858 10858 Unknown 1 1 iPad 3+ Used 0 None
## 1243 11244 Unknown 1 0 Unknown Used Unknown Unknown
## 2135 12137 Unknown 1 NA iPad 1 Used 1 AT&T
## storage
## 631 16
## 683 32
## 858 16
## 1243 Unknown
## 2135 16
## descr.my
## 631 Very good condition. Minor bumps and bruises. Only scratches on screen are in non- viewing black
## 683 Comes with folding black case and is engraved in small letters on the back. Still works perfectly
## 858 screen cracked. name engraving in the back (blacked out)
## 1243 Ipad is in fair condition. Minor scratches on back. Edge around screen is black instead of white.
## 2135 Device is in AVERAGE used cosmetic condition with heavy scratches and wear. Color is black . Device isglb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"
print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
"cellular", "carrier", "storage", "descr.my")])## UniqueID color D.P.spacegray prdline.my condition cellular carrier
## 2104 12106 Unknown 1 iPadAir Used 0 None
## storage
## 2104 16
## descr.my
## 2104 This is an iPad Air first generation (spacegray color). It's a used iPad (just like new) as shown in theglb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"
print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
"cellular", "carrier", "storage", "descr.my")])## UniqueID color D.P.white prdline.my condition
## 573 10573 Unknown 1 iPadmini 2+ Used
## 809 10809 Unknown 1 iPad 3+ Used
## 925 10925 Unknown 1 iPadmini 2+ Used
## 1243 11244 Unknown 1 Unknown Used
## 1734 11735 Unknown 1 iPad 3+ For parts or not working
## cellular carrier storage
## 573 0 None 16
## 809 0 None 64
## 925 0 None 64
## 1243 Unknown Unknown Unknown
## 1734 1 Verizon 16
## descr.my
## 573 Like new white iPad mini no scratches always kept in case, sold with keyboard, box and cords
## 809 iPad 3 gen. 64GB, white, wifi- only. Condition = good as new, very minor sign of use. No charger.
## 925 iPad mini 2/ Retina Display/ Latest Model/ 64GB/ Wi- Fi/ Silver&White . Near Mint Condition excellent
## 1243 Ipad is in fair condition. Minor scratches on back. Edge around screen is black instead of white.
## 1734 Device is in POOR used cosmetic condition with cracked outer glass. Color is White. Device isglb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
"White"
glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 36.550 106.523 69.973
## 10 extract.features_end 9 0 106.524 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 8 extract.features_bind.DTM 7
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 36.550 106.523 69.973 69.973
## 5 0 20.859 31.865 11.006 11.006
## 7 0 33.730 36.056 2.326 2.326
## 3 0 18.786 20.853 2.068 2.067
## 6 0 31.865 33.729 1.864 1.864
## 2 0 17.407 18.786 1.379 1.379
## 8 0 36.057 36.549 0.493 0.492
## 1 0 17.386 17.406 0.020 0.020
## 4 1 20.854 20.858 0.005 0.004
## [1] "Total Elapsed Time: 106.523 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 17.380 107.851 90.471
## 6 cluster.data 4 0 107.851 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 107.851 109.011 1.16
## 7 manage.missing.data 4 1 109.012 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1600 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.cosmet D.T.hous
## 1521 2542 2585
## D.T.X100 D.T.list D.T.tab
## 2593 2616 2630
## D.T.near D.T.fair D.T.slight
## 2623 2635 2645
## D.T.absolut D.T.profession D.nwrds.log
## 2645 2641 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2427
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2538
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air D.P.black D.P.white
## 2636 2640 2647
## D.P.gold D.P.spacegray
## 2655 2650
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1600 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.cosmet D.T.hous
## 1521 2542 2585
## D.T.X100 D.T.list D.T.tab
## 2593 2616 2630
## D.T.near D.T.fair D.T.slight
## 2623 2635 2645
## D.T.absolut D.T.profession D.nwrds.log
## 2645 2641 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2427
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2538
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air D.P.black D.P.white
## 2636 2640 2647
## D.P.gold D.P.spacegray
## 2655 2650
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print("Clustering features: ")
print(cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_cluster_entropy_var]),
method="ML")))
category_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, glb_cluster_entropy_var]))
names(category_df)[c(1, 2)] <- c(glb_category_var, glb_cluster_entropy_var)
category_df <- do.call(tidyr::spread,
list(category_df, glb_cluster_entropy_var, "Freq"))
tmp.entropy <- sapply(1:nrow(category_df),
function(row) entropy(as.numeric(category_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(category_df),
function(row) sum(as.numeric(category_df[row, -1])))
category_df$.entropy <- tmp.entropy; category_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)", glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
print(category_df)
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (grp in sort(unique(glb_allobs_df[, glb_category_var]))) {
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
ctgry_allobs_df[, glb_cluster_entropy_var] <-
as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
# Float representations issue -2.22e-16 vs. 0.0000
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glb_txt_vars,
cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df[, glb_category_var].fctr) / minClusterSize=20)
# which(levels(glb_allobs_df[, glb_category_var].fctr) == grp)
# table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
cluster_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, ".clusterid"],
glb_allobs_df[, glb_cluster_entropy_var]))
cluster_df <- subset(cluster_df, Freq > 0)
names(cluster_df)[c(1, 2, 3)] <- c(glb_category_var, ".clusterid", glb_cluster_entropy_var)
# spread(unite(cluster_df, prdline.my.clusterid, prdline.my, .clusterid),
# sold.fctr, Freq)
cluster_df <- do.call(tidyr::unite,
list(cluster_df, paste0(glb_category_var, ".clusterid"),
grep(glb_category_var, names(cluster_df)),
grep(".clusterid", names(cluster_df))))
cluster_df <- do.call(tidyr::spread,
list(cluster_df, glb_cluster_entropy_var, "Freq"))
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
print(cluster_df)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features[paste0(glb_category_var, ".fctr")] <-
c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr## [1] "Clustering features: "
## [1] "D.T.cosmet" "D.T.hous" "D.T.X100" "D.T.list"
## [5] "D.T.tab" "D.T.near" "D.T.fair" "D.T.slight"
## [9] "D.T.absolut" "D.T.profession" "D.P.http" "D.P.mini"
## [13] "D.P.air" "D.P.black" "D.P.white" "D.P.gold"
## [17] "D.P.spacegray"
## [1] "glb_allobs_df Entropy: 0.6903"
## [1] "glb_allobs_df$prdline.my Entropy: 0.6850 (99.2280 pct)"
## prdline.my 0 1 .entropy .knt
## 1 Unknown 118 80 0.6746159 198
## 2 iPad 1 100 125 0.6869616 225
## 3 iPad 2 141 147 0.6929302 288
## 4 iPad 3+ 166 145 0.6908657 311
## 5 iPadAir 203 150 0.6818332 353
## 6 iPadmini 146 133 0.6920612 279
## 7 iPadmini 2+ 125 80 0.6688571 205
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 5 10005 0 Unknown
## 24 10024 0 Unknown
## descr.my
## 5 Please feel free to buy. All product have been thoroughly inspected, cleaned and tested to be 100%
## 24
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 5 0 0 0.5375583 0 0 0 0
## 24 0 0 0.0000000 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0
## 24 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 5 0 0 0 0
## 24 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold prdline.my
## 1029 11029 0 Unknown
## 1077 11077 0 Unknown
## descr.my
## 1029 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## 1077 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 1029 0.4118266 0.4732416 0 0.5470937 0 0.5716472 0
## 1077 0.4118266 0.4732416 0 0.5470937 0 0.5716472 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 1029 0 0 0 0 0 0
## 1077 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 1029 0 0 0 0
## 1077 0 0 0 0
## [1] "Category: iPad 1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 9 10009 1 iPad 1
## 110 10110 0 iPad 1
## descr.my
## 9
## 110 iPads are in great shape. Very minor cosmetic scuffs on the back. Screens are in excellent shape.
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 9 0.0000000 0 0 0 0 0 0
## 110 0.4530092 0 0 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 9 0 0 0 0 0 0
## 110 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 9 0 0 0 0
## 110 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold prdline.my
## 586 10586 0 iPad 1
## 2345 12347 <NA> iPad 1
## descr.my
## 586 Those used TAB are in good condition with minor scratches on the housing or screen (when the phone
## 2345 This used TAB is in good condition with minor scratches on the housing or screen (when the phone is
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 586 0 0.5784064 0 0 0.7356328 0 0
## 2345 0 0.5784064 0 0 0.7356328 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 586 0 0 0 0 0 0
## 2345 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 586 0 0 0 0
## 2345 0 0 0 0
## [1] "Category: iPad 2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 1 10001 0 iPad 2
## 2 10002 1 iPad 2
## descr.my
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 1 1.510031 0 0 0 0 0 0
## 2 0.000000 0 0 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 1 0 0 0 0
## 2 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold prdline.my
## 132 10132 0 iPad 2
## 1766 11767 0 iPad 2
## descr.my
## 132 Overall good condition. Some wear from use. Scratches/ scuffs/ nicks/ scrapes on unit housing back,
## 1766 Ipad 2 32gb Housing. Some scratches and small dents, but overall good condition.
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 132 0 0.4338048 0 0 0 0 0
## 1766 0 0.5784064 0 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 132 0 0 0 0 0 0
## 1766 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 132 0 0 0 0
## 1766 0 0 0 0
## [1] "Category: iPad 3+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 3 10003 1 iPad 3+
## 28 10028 0 iPad 3+
## descr.my
## 3
## 28 Ipad is used, in very good cosmetic condition. Has some very minor scratches on the body from normal
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 3 0.0000000 0 0 0 0 0 0
## 28 0.5033436 0 0 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 3 0 0 0 0 0 0
## 28 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 3 0 0 0 0
## 28 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold prdline.my
## 104 10104 0 iPad 3+
## 144 10144 0 iPad 3+
## descr.my
## 104 This TAB is in average condition with some scratches on the housing or screen (does not affect
## 144 This TAB is in good condition with minor scratches on the housing or screen (does not affect
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 104 0 0.7436654 0 0 0.9458136 0 0
## 144 0 0.6507072 0 0 0.8275869 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 104 0 0 0 0 0 0
## 144 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 104 0 0 0 0
## 144 0 0 0 0
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 16 10016 0 iPadAir
## 44 10044 0 iPadAir
## descr.my
## 16
## 44 Open Box Units Grade A Condition. Units may contain minor cosmetic imperfections.
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 16 0.0000000 0 0 0 0 0 0
## 44 0.4118266 0 0 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 16 0 0 0 0 0 0
## 44 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 16 0 0 0 0
## 44 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold prdline.my
## 139 10139 0 iPadAir
## 1373 11374 0 iPadAir
## descr.my
## 139 C Stock - Seller refurbished & in Fair condition. 100% Fully Functional, Clean IMEI & iCloud,
## 1373 C Stock - Seller refurbished & in Fair condition. 100% Fully Functional, Clean IMEI (ESN) & iCloud,
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 139 0 0 0.4886893 0 0 0 0.6287410
## 1373 0 0 0.4479652 0 0 0 0.5763459
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 139 0 0 0 0 0 0
## 1373 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 139 0 0 0 0
## 1373 0 0 0 0
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 7 10007 1 iPadmini
## 93 10093 0 iPadmini
## descr.my
## 7
## 93 *FREE* Same- Day Ship | 90- Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 7 0 0 0.0000000 0 0 0 0
## 93 0 0 0.4886893 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 7 0 0 0 0 0 0
## 93 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 7 0 0 0 0
## 93 0 0 0 0
## [1] "min distance(0.0000) pair:"
## UniqueID sold prdline.my descr.my D.T.cosmet D.T.hous D.T.X100 D.T.list
## 7 10007 1 iPadmini 0 0 0 0
## 57 10057 0 iPadmini 0 0 0 0
## D.T.tab D.T.near D.T.fair D.T.slight D.T.absolut D.T.profession
## 7 0 0 0 0 0 0
## 57 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 7 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0
## [1] "Category: iPadmini 2+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdline.my
## 4 10004 0 iPadmini 2+
## 26 10026 0 iPadmini 2+
## descr.my
## 4
## 26 iPad mini 2 is an open box item, good condition. Does not include original box. Include Genuine
## D.T.cosmet D.T.hous D.T.X100 D.T.list D.T.tab D.T.near D.T.fair
## 4 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0
## D.T.slight D.T.absolut D.T.profession D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0
## 26 0 0 0 0 1 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 4 0 0 0 0
## 26 0 0 0 0
## [1] "min distance(0.0000) pair:"
## UniqueID sold prdline.my descr.my D.T.cosmet D.T.hous D.T.X100 D.T.list
## 4 10004 0 iPadmini 2+ 0 0 0 0
## 6 10006 1 iPadmini 2+ 0 0 0 0
## D.T.tab D.T.near D.T.fair D.T.slight D.T.absolut D.T.profession D.P.http
## 4 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 4 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## [1] "glb_allobs_df$prdline.my$.clusterid Entropy: 0.6661 (97.2330 pct)"
## prdline.my.clusterid 0 1 .entropy .knt
## 1 Unknown_1 104 71 0.6752607 175
## 2 Unknown_2 14 9 0.6693280 23
## 3 iPad 1_1 84 120 0.6774944 204
## 4 iPad 1_2 16 5 0.5488744 21
## 5 iPad 2_1 111 144 0.6847499 255
## 6 iPad 2_2 30 3 0.3046361 33
## 7 iPad 3+_1 125 137 0.6920979 262
## 8 iPad 3+_2 30 3 0.3046361 33
## 9 iPad 3+_3 11 5 0.6210864 16
## 10 iPadAir_1 181 136 0.6830373 317
## 11 iPadAir_2 9 9 0.6931472 18
## 12 iPadAir_3 13 5 0.5908422 18
## 13 iPadmini 2+_1 113 75 0.6725780 188
## 14 iPadmini 2+_2 12 5 0.6057975 17
## 15 iPadmini_1 132 115 0.6907768 247
## 16 iPadmini_2 14 18 0.6853142 32# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 109.012 112.831 3.819
## 8 select.features 5 0 112.831 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## startprice.log startprice.log 0.714953462
## biddable biddable -0.478968707
## prdline.my.fctr prdline.my.fctr 0.291582777
## condition.fctr condition.fctr 0.205950857
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds -0.137915070
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -0.135554973
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.134320942
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -0.131863055
## D.sum.TfIdf D.sum.TfIdf -0.131863055
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio 0.131404106
## D.npnct24.log D.npnct24.log -0.130780343
## D.nuppr.log D.nuppr.log -0.117520055
## D.nchrs.log D.nchrs.log -0.116797803
## D.terms.n.post.stem.log D.terms.n.post.stem.log -0.112041195
## D.nwrds.unq.log D.nwrds.unq.log -0.112041195
## D.terms.n.post.stop.log D.terms.n.post.stop.log -0.111516189
## color.fctr color.fctr 0.106584795
## D.nwrds.log D.nwrds.log -0.102467567
## D.terms.n.post.stem D.terms.n.post.stem -0.093730199
## D.terms.n.post.stop D.terms.n.post.stop -0.092986239
## D.npnct13.log D.npnct13.log -0.083057559
## D.npnct11.log D.npnct11.log -0.080106895
## D.nstopwrds.log D.nstopwrds.log -0.071562991
## carrier.fctr carrier.fctr 0.068436492
## D.npnct03.log D.npnct03.log -0.054044001
## D.npnct06.log D.npnct06.log -0.048540915
## D.npnct12.log D.npnct12.log -0.042554399
## storage.fctr storage.fctr 0.039665265
## D.T.near D.T.near -0.037040503
## D.npnct16.log D.npnct16.log -0.035504765
## D.P.black D.P.black 0.035226290
## D.npnct10.log D.npnct10.log 0.034091652
## D.npnct07.log D.npnct07.log -0.032922510
## D.T.absolut D.T.absolut -0.029441095
## D.P.spacegray D.P.spacegray -0.026687820
## D.P.air D.P.air -0.025982300
## D.npnct01.log D.npnct01.log -0.024992771
## D.npnct14.log D.npnct14.log 0.023604586
## D.npnct15.log D.npnct15.log -0.023187752
## D.P.white D.P.white -0.019344180
## D.P.mini D.P.mini -0.017493744
## D.npnct05.log D.npnct05.log -0.015097464
## D.npnct08.log D.npnct08.log -0.014183822
## D.T.cosmet D.T.cosmet 0.014129381
## D.T.X100 D.T.X100 -0.012599180
## D.T.tab D.T.tab 0.011863019
## D.T.hous D.T.hous 0.009945381
## UniqueID UniqueID -0.009667837
## idseq.my idseq.my -0.009667837
## D.T.list D.T.list 0.008687013
## .clusterid .clusterid 0.008642022
## .clusterid.fctr .clusterid.fctr 0.008642022
## .rnorm .rnorm -0.008500798
## D.ndgts.log D.ndgts.log -0.007780470
## cellular.fctr cellular.fctr 0.006550796
## D.T.slight D.T.slight 0.004977747
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.001641656
## sold sold NA
## D.T.fair D.T.fair NA
## D.T.profession D.T.profession NA
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct09.log D.npnct09.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct18.log D.npnct18.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct28.log D.npnct28.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## D.P.gold D.P.gold NA
## exclude.as.feat cor.y.abs
## startprice.log 1 0.714953462
## biddable 0 0.478968707
## prdline.my.fctr 0 0.291582777
## condition.fctr 0 0.205950857
## D.ratio.sum.TfIdf.nwrds 0 0.137915070
## D.TfIdf.sum.post.stop 0 0.135554973
## D.ratio.nstopwrds.nwrds 0 0.134320942
## D.TfIdf.sum.post.stem 0 0.131863055
## D.sum.TfIdf 0 0.131863055
## D.TfIdf.sum.stem.stop.Ratio 0 0.131404106
## D.npnct24.log 0 0.130780343
## D.nuppr.log 0 0.117520055
## D.nchrs.log 0 0.116797803
## D.terms.n.post.stem.log 0 0.112041195
## D.nwrds.unq.log 0 0.112041195
## D.terms.n.post.stop.log 0 0.111516189
## color.fctr 0 0.106584795
## D.nwrds.log 0 0.102467567
## D.terms.n.post.stem 0 0.093730199
## D.terms.n.post.stop 0 0.092986239
## D.npnct13.log 0 0.083057559
## D.npnct11.log 0 0.080106895
## D.nstopwrds.log 0 0.071562991
## carrier.fctr 0 0.068436492
## D.npnct03.log 0 0.054044001
## D.npnct06.log 0 0.048540915
## D.npnct12.log 0 0.042554399
## storage.fctr 0 0.039665265
## D.T.near 1 0.037040503
## D.npnct16.log 0 0.035504765
## D.P.black 1 0.035226290
## D.npnct10.log 0 0.034091652
## D.npnct07.log 0 0.032922510
## D.T.absolut 1 0.029441095
## D.P.spacegray 1 0.026687820
## D.P.air 1 0.025982300
## D.npnct01.log 0 0.024992771
## D.npnct14.log 0 0.023604586
## D.npnct15.log 0 0.023187752
## D.P.white 1 0.019344180
## D.P.mini 1 0.017493744
## D.npnct05.log 0 0.015097464
## D.npnct08.log 0 0.014183822
## D.T.cosmet 1 0.014129381
## D.T.X100 1 0.012599180
## D.T.tab 1 0.011863019
## D.T.hous 1 0.009945381
## UniqueID 1 0.009667837
## idseq.my 0 0.009667837
## D.T.list 1 0.008687013
## .clusterid 1 0.008642022
## .clusterid.fctr 0 0.008642022
## .rnorm 0 0.008500798
## D.ndgts.log 0 0.007780470
## cellular.fctr 0 0.006550796
## D.T.slight 1 0.004977747
## D.terms.n.stem.stop.Ratio 0 0.001641656
## sold 1 NA
## D.T.fair 1 NA
## D.T.profession 1 NA
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct09.log 0 NA
## D.npnct17.log 0 NA
## D.npnct18.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct28.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 1 NA
## D.P.gold 1 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(startprice, D.TfIdf.sum.post.stem)=-0.1319"
## [1] "cor(startprice, D.sum.TfIdf)=-0.1319"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stem.log)=1.0000"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1120"
## [1] "cor(startprice, D.terms.n.post.stem.log)=-0.1120"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop.log)=0.9998"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1120"
## [1] "cor(startprice, D.terms.n.post.stop.log)=-0.1115"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9998"
## [1] "cor(startprice, D.nchrs.log)=-0.1168"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nuppr.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0937"
## [1] "cor(startprice, D.terms.n.post.stop)=-0.0930"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop as highly correlated with
## D.terms.n.post.stem## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9980"
## [1] "cor(startprice, D.TfIdf.sum.post.stem)=-0.1319"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1356"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.TfIdf.sum.post.stop## [1] "cor(D.nuppr.log, D.nwrds.unq.log)=0.9927"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1120"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.nuppr.log## [1] "cor(D.nuppr.log, D.nwrds.log)=0.9910"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"
## [1] "cor(startprice, D.nwrds.log)=-0.1025"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nuppr.log## [1] "cor(D.npnct24.log, D.nuppr.log)=0.9792"
## [1] "cor(startprice, D.npnct24.log)=-0.1308"
## [1] "cor(startprice, D.nuppr.log)=-0.1175"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.npnct24.log## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9653"
## [1] "cor(startprice, D.npnct24.log)=-0.1308"
## [1] "cor(startprice, D.ratio.nstopwrds.nwrds)=0.1343"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9445"
## [1] "cor(startprice, D.npnct06.log)=-0.0485"
## [1] "cor(startprice, D.npnct16.log)=-0.0355"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.TfIdf.sum.post.stop, D.ratio.nstopwrds.nwrds)=-0.9234"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1356"
## [1] "cor(startprice, D.ratio.nstopwrds.nwrds)=0.1343"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.TfIdf.sum.post.stop## [1] "cor(D.nstopwrds.log, D.terms.n.post.stem)=0.9050"
## [1] "cor(startprice, D.nstopwrds.log)=-0.0716"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0937"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.terms.n.post.stem## [1] "cor(D.TfIdf.sum.post.stop, D.terms.n.post.stem)=0.8859"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1356"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0937"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.TfIdf.sum.post.stop## [1] "cor(D.npnct03.log, D.npnct06.log)=0.7921"
## [1] "cor(startprice, D.npnct03.log)=-0.0540"
## [1] "cor(startprice, D.npnct06.log)=-0.0485"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct06.log as highly correlated with
## D.npnct03.log## id cor.y exclude.as.feat cor.y.abs
## 77 startprice.log 0.714953462 1 0.714953462
## 75 prdline.my.fctr 0.291582777 0 0.291582777
## 73 condition.fctr 0.205950857 0 0.205950857
## 60 D.ratio.nstopwrds.nwrds 0.134320942 0 0.134320942
## 23 D.TfIdf.sum.stem.stop.Ratio 0.131404106 0 0.131404106
## 72 color.fctr 0.106584795 0 0.106584795
## 70 carrier.fctr 0.068436492 0 0.068436492
## 78 storage.fctr 0.039665265 0 0.039665265
## 5 D.P.black 0.035226290 1 0.035226290
## 35 D.npnct10.log 0.034091652 0 0.034091652
## 39 D.npnct14.log 0.023604586 0 0.023604586
## 13 D.T.cosmet 0.014129381 1 0.014129381
## 20 D.T.tab 0.011863019 1 0.011863019
## 15 D.T.hous 0.009945381 1 0.009945381
## 16 D.T.list 0.008687013 1 0.008687013
## 1 .clusterid 0.008642022 1 0.008642022
## 2 .clusterid.fctr 0.008642022 0 0.008642022
## 71 cellular.fctr 0.006550796 0 0.006550796
## 19 D.T.slight 0.004977747 1 0.004977747
## 67 D.terms.n.stem.stop.Ratio -0.001641656 0 0.001641656
## 25 D.ndgts.log -0.007780470 0 0.007780470
## 3 .rnorm -0.008500798 0 0.008500798
## 68 UniqueID -0.009667837 1 0.009667837
## 74 idseq.my -0.009667837 0 0.009667837
## 11 D.T.X100 -0.012599180 1 0.012599180
## 33 D.npnct08.log -0.014183822 0 0.014183822
## 30 D.npnct05.log -0.015097464 0 0.015097464
## 8 D.P.mini -0.017493744 1 0.017493744
## 10 D.P.white -0.019344180 1 0.019344180
## 40 D.npnct15.log -0.023187752 0 0.023187752
## 26 D.npnct01.log -0.024992771 0 0.024992771
## 4 D.P.air -0.025982300 1 0.025982300
## 9 D.P.spacegray -0.026687820 1 0.026687820
## 12 D.T.absolut -0.029441095 1 0.029441095
## 32 D.npnct07.log -0.032922510 0 0.032922510
## 41 D.npnct16.log -0.035504765 0 0.035504765
## 17 D.T.near -0.037040503 1 0.037040503
## 37 D.npnct12.log -0.042554399 0 0.042554399
## 31 D.npnct06.log -0.048540915 0 0.048540915
## 28 D.npnct03.log -0.054044001 0 0.054044001
## 56 D.nstopwrds.log -0.071562991 0 0.071562991
## 36 D.npnct11.log -0.080106895 0 0.080106895
## 38 D.npnct13.log -0.083057559 0 0.083057559
## 65 D.terms.n.post.stop -0.092986239 0 0.092986239
## 63 D.terms.n.post.stem -0.093730199 0 0.093730199
## 58 D.nwrds.log -0.102467567 0 0.102467567
## 66 D.terms.n.post.stop.log -0.111516189 0 0.111516189
## 59 D.nwrds.unq.log -0.112041195 0 0.112041195
## 64 D.terms.n.post.stem.log -0.112041195 0 0.112041195
## 24 D.nchrs.log -0.116797803 0 0.116797803
## 57 D.nuppr.log -0.117520055 0 0.117520055
## 49 D.npnct24.log -0.130780343 0 0.130780343
## 21 D.TfIdf.sum.post.stem -0.131863055 0 0.131863055
## 62 D.sum.TfIdf -0.131863055 0 0.131863055
## 22 D.TfIdf.sum.post.stop -0.135554973 0 0.135554973
## 61 D.ratio.sum.TfIdf.nwrds -0.137915070 0 0.137915070
## 69 biddable -0.478968707 0 0.478968707
## 6 D.P.gold NA 1 NA
## 7 D.P.http NA 1 NA
## 14 D.T.fair NA 1 NA
## 18 D.T.profession NA 1 NA
## 27 D.npnct02.log NA 0 NA
## 29 D.npnct04.log NA 0 NA
## 34 D.npnct09.log NA 0 NA
## 42 D.npnct17.log NA 0 NA
## 43 D.npnct18.log NA 0 NA
## 44 D.npnct19.log NA 0 NA
## 45 D.npnct20.log NA 0 NA
## 46 D.npnct21.log NA 0 NA
## 47 D.npnct22.log NA 0 NA
## 48 D.npnct23.log NA 0 NA
## 50 D.npnct25.log NA 0 NA
## 51 D.npnct26.log NA 0 NA
## 52 D.npnct27.log NA 0 NA
## 53 D.npnct28.log NA 0 NA
## 54 D.npnct29.log NA 0 NA
## 55 D.npnct30.log NA 0 NA
## 76 sold NA 1 NA
## cor.high.X freqRatio percentUnique zeroVar nzv myNearZV
## 77 <NA> 4.000000 28.3720930 FALSE FALSE FALSE
## 75 <NA> 1.020408 0.8139535 FALSE FALSE FALSE
## 73 <NA> 5.528302 0.6976744 FALSE FALSE FALSE
## 60 D.TfIdf.sum.post.stop 15.176471 7.7906977 FALSE FALSE FALSE
## 23 <NA> 106.000000 33.9534884 FALSE FALSE FALSE
## 72 <NA> 1.500000 0.5813953 FALSE FALSE FALSE
## 70 <NA> 4.105263 0.8139535 FALSE FALSE FALSE
## 78 <NA> 2.917722 0.5813953 FALSE FALSE FALSE
## 5 <NA> 171.000000 0.2325581 FALSE TRUE FALSE
## 35 <NA> 429.000000 0.2325581 FALSE TRUE TRUE
## 39 <NA> 65.076923 0.3488372 FALSE TRUE FALSE
## 13 <NA> 119.714286 0.8139535 FALSE TRUE FALSE
## 20 <NA> 859.000000 0.2325581 FALSE TRUE TRUE
## 15 <NA> 857.000000 0.4651163 FALSE TRUE TRUE
## 16 <NA> 857.000000 0.4651163 FALSE TRUE TRUE
## 1 <NA> 15.346154 0.3488372 FALSE FALSE FALSE
## 2 <NA> 15.346154 0.3488372 FALSE FALSE FALSE
## 71 <NA> 2.405286 0.3488372 FALSE FALSE FALSE
## 19 <NA> 425.500000 0.8139535 FALSE TRUE TRUE
## 67 <NA> 104.250000 0.9302326 FALSE TRUE FALSE
## 25 <NA> 38.190476 1.2790698 FALSE TRUE FALSE
## 3 <NA> 1.000000 100.0000000 FALSE FALSE FALSE
## 68 <NA> 1.000000 100.0000000 FALSE FALSE FALSE
## 74 <NA> 1.000000 100.0000000 FALSE FALSE FALSE
## 11 <NA> 428.000000 0.4651163 FALSE TRUE TRUE
## 33 <NA> 70.416667 0.3488372 FALSE TRUE FALSE
## 30 <NA> 214.000000 0.2325581 FALSE TRUE FALSE
## 8 <NA> 121.714286 0.3488372 FALSE TRUE FALSE
## 10 <NA> 213.750000 0.3488372 FALSE TRUE FALSE
## 40 <NA> 94.111111 0.3488372 FALSE TRUE FALSE
## 26 <NA> 48.941176 0.5813953 FALSE TRUE FALSE
## 4 <NA> 121.857143 0.2325581 FALSE TRUE FALSE
## 9 <NA> 429.000000 0.2325581 FALSE TRUE TRUE
## 12 <NA> 426.000000 0.8139535 FALSE TRUE TRUE
## 32 <NA> 859.000000 0.2325581 FALSE TRUE TRUE
## 41 D.npnct06.log 52.125000 0.3488372 FALSE TRUE FALSE
## 17 <NA> 858.000000 0.3488372 FALSE TRUE TRUE
## 37 <NA> 30.703704 0.3488372 FALSE TRUE FALSE
## 31 D.npnct03.log 64.461538 0.3488372 FALSE TRUE FALSE
## 28 <NA> 84.500000 0.3488372 FALSE TRUE FALSE
## 56 D.terms.n.post.stem 15.216216 1.7441860 FALSE FALSE FALSE
## 36 <NA> 9.701299 0.8139535 FALSE FALSE FALSE
## 38 <NA> 5.935780 0.6976744 FALSE FALSE FALSE
## 65 D.terms.n.post.stem 10.320000 1.6279070 FALSE FALSE FALSE
## 63 D.TfIdf.sum.post.stop 9.735849 1.6279070 FALSE FALSE FALSE
## 58 D.nuppr.log 16.125000 2.7906977 FALSE FALSE FALSE
## 66 D.nwrds.unq.log 10.320000 1.6279070 FALSE FALSE FALSE
## 59 D.nuppr.log 9.735849 1.6279070 FALSE FALSE FALSE
## 64 D.nwrds.unq.log 9.735849 1.6279070 FALSE FALSE FALSE
## 24 D.nuppr.log 20.640000 10.6976744 FALSE FALSE FALSE
## 57 D.npnct24.log 19.111111 8.6046512 FALSE TRUE FALSE
## 49 D.ratio.nstopwrds.nwrds 1.500000 0.2325581 FALSE FALSE FALSE
## 21 D.TfIdf.sum.post.stop 103.200000 35.2325581 FALSE FALSE FALSE
## 62 D.TfIdf.sum.post.stem 103.200000 35.2325581 FALSE FALSE FALSE
## 22 <NA> 103.200000 35.2325581 FALSE FALSE FALSE
## 61 <NA> 103.200000 35.5813953 FALSE FALSE FALSE
## 69 <NA> 2.909091 0.2325581 FALSE FALSE FALSE
## 6 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 7 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 14 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 18 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 27 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 29 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 34 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 42 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 43 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 44 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 45 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 46 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 47 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 48 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 50 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 51 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 52 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 53 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 54 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 55 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## 76 <NA> 0.000000 0.1162791 TRUE TRUE TRUE
## is.cor.y.abs.low
## 77 FALSE
## 75 FALSE
## 73 FALSE
## 60 FALSE
## 23 FALSE
## 72 FALSE
## 70 FALSE
## 78 FALSE
## 5 FALSE
## 35 FALSE
## 39 FALSE
## 13 FALSE
## 20 FALSE
## 15 FALSE
## 16 FALSE
## 1 FALSE
## 2 FALSE
## 71 TRUE
## 19 TRUE
## 67 TRUE
## 25 TRUE
## 3 FALSE
## 68 FALSE
## 74 FALSE
## 11 FALSE
## 33 FALSE
## 30 FALSE
## 8 FALSE
## 10 FALSE
## 40 FALSE
## 26 FALSE
## 4 FALSE
## 9 FALSE
## 12 FALSE
## 32 FALSE
## 41 FALSE
## 17 FALSE
## 37 FALSE
## 31 FALSE
## 28 FALSE
## 56 FALSE
## 36 FALSE
## 38 FALSE
## 65 FALSE
## 63 FALSE
## 58 FALSE
## 66 FALSE
## 59 FALSE
## 64 FALSE
## 24 FALSE
## 57 FALSE
## 49 FALSE
## 21 FALSE
## 62 FALSE
## 22 FALSE
## 61 FALSE
## 69 FALSE
## 6 NA
## 7 NA
## 14 NA
## 18 NA
## 27 NA
## 29 NA
## 34 NA
## 42 NA
## 43 NA
## 44 NA
## 45 NA
## 46 NA
## 47 NA
## 48 NA
## 50 NA
## 51 NA
## 52 NA
## 53 NA
## 54 NA
## 55 NA
## 76 NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 35 D.npnct10.log 0.034091652 0 0.034091652 <NA>
## 20 D.T.tab 0.011863019 1 0.011863019 <NA>
## 15 D.T.hous 0.009945381 1 0.009945381 <NA>
## 16 D.T.list 0.008687013 1 0.008687013 <NA>
## 19 D.T.slight 0.004977747 1 0.004977747 <NA>
## 11 D.T.X100 -0.012599180 1 0.012599180 <NA>
## 9 D.P.spacegray -0.026687820 1 0.026687820 <NA>
## 12 D.T.absolut -0.029441095 1 0.029441095 <NA>
## 32 D.npnct07.log -0.032922510 0 0.032922510 <NA>
## 17 D.T.near -0.037040503 1 0.037040503 <NA>
## 6 D.P.gold NA 1 NA <NA>
## 7 D.P.http NA 1 NA <NA>
## 14 D.T.fair NA 1 NA <NA>
## 18 D.T.profession NA 1 NA <NA>
## 27 D.npnct02.log NA 0 NA <NA>
## 29 D.npnct04.log NA 0 NA <NA>
## 34 D.npnct09.log NA 0 NA <NA>
## 42 D.npnct17.log NA 0 NA <NA>
## 43 D.npnct18.log NA 0 NA <NA>
## 44 D.npnct19.log NA 0 NA <NA>
## 45 D.npnct20.log NA 0 NA <NA>
## 46 D.npnct21.log NA 0 NA <NA>
## 47 D.npnct22.log NA 0 NA <NA>
## 48 D.npnct23.log NA 0 NA <NA>
## 50 D.npnct25.log NA 0 NA <NA>
## 51 D.npnct26.log NA 0 NA <NA>
## 52 D.npnct27.log NA 0 NA <NA>
## 53 D.npnct28.log NA 0 NA <NA>
## 54 D.npnct29.log NA 0 NA <NA>
## 55 D.npnct30.log NA 0 NA <NA>
## 76 sold NA 1 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 35 429.0 0.2325581 FALSE TRUE TRUE FALSE
## 20 859.0 0.2325581 FALSE TRUE TRUE FALSE
## 15 857.0 0.4651163 FALSE TRUE TRUE FALSE
## 16 857.0 0.4651163 FALSE TRUE TRUE FALSE
## 19 425.5 0.8139535 FALSE TRUE TRUE TRUE
## 11 428.0 0.4651163 FALSE TRUE TRUE FALSE
## 9 429.0 0.2325581 FALSE TRUE TRUE FALSE
## 12 426.0 0.8139535 FALSE TRUE TRUE FALSE
## 32 859.0 0.2325581 FALSE TRUE TRUE FALSE
## 17 858.0 0.3488372 FALSE TRUE TRUE FALSE
## 6 0.0 0.1162791 TRUE TRUE TRUE NA
## 7 0.0 0.1162791 TRUE TRUE TRUE NA
## 14 0.0 0.1162791 TRUE TRUE TRUE NA
## 18 0.0 0.1162791 TRUE TRUE TRUE NA
## 27 0.0 0.1162791 TRUE TRUE TRUE NA
## 29 0.0 0.1162791 TRUE TRUE TRUE NA
## 34 0.0 0.1162791 TRUE TRUE TRUE NA
## 42 0.0 0.1162791 TRUE TRUE TRUE NA
## 43 0.0 0.1162791 TRUE TRUE TRUE NA
## 44 0.0 0.1162791 TRUE TRUE TRUE NA
## 45 0.0 0.1162791 TRUE TRUE TRUE NA
## 46 0.0 0.1162791 TRUE TRUE TRUE NA
## 47 0.0 0.1162791 TRUE TRUE TRUE NA
## 48 0.0 0.1162791 TRUE TRUE TRUE NA
## 50 0.0 0.1162791 TRUE TRUE TRUE NA
## 51 0.0 0.1162791 TRUE TRUE TRUE NA
## 52 0.0 0.1162791 TRUE TRUE TRUE NA
## 53 0.0 0.1162791 TRUE TRUE TRUE NA
## 54 0.0 0.1162791 TRUE TRUE TRUE NA
## 55 0.0 0.1162791 TRUE TRUE TRUE NA
## 76 0.0 0.1162791 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
## biddable startprice.log cellular.fctr
## 1444 31 1600
## D.terms.n.post.stop D.terms.n.post.stop.log D.TfIdf.sum.post.stop
## 1521 1521 1521
## D.terms.n.post.stem D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 1521 1521 1521
## D.T.cosmet D.nwrds.log D.nwrds.unq.log
## 2542 1520 1521
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log
## 1521 1521 1520
## D.nuppr.log D.ndgts.log D.npnct01.log
## 1522 2427 2579
## D.npnct03.log D.npnct05.log D.npnct06.log
## 2614 2592 2554
## D.npnct08.log D.npnct11.log D.npnct12.log
## 2581 2301 2538
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct24.log D.nstopwrds.log
## 2546 1520 1663
## D.P.mini D.P.air D.P.black
## 2623 2636 2640
## D.P.white
## 2647
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 112.831 116.123 3.292
## 9 partition.data.training 6 0 116.123 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for startprice; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 5 iPadAir 340 340 0.1892042 0.1892042
## 3 iPad 2 295 295 0.1641625 0.1641625
## 4 iPad 3+ 289 289 0.1608236 0.1608236
## 6 iPadmini 260 260 0.1446856 0.1446856
## 7 iPadmini 2+ 219 219 0.1218698 0.1218698
## 1 Unknown 205 205 0.1140790 0.1140790
## 2 iPad 1 189 189 0.1051753 0.1051753# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 78 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## startprice startprice TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 68 UniqueID -0.009667837 TRUE 0.009667837 <NA>
## startprice startprice NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 68 1 100 FALSE FALSE FALSE FALSE
## startprice NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 68 <NA> FALSE TRUE NA
## startprice <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct10.log" "D.T.tab" "D.T.hous" "D.T.list"
## [5] "D.T.slight" "D.T.X100" "D.P.spacegray" "D.T.absolut"
## [9] "D.npnct07.log" "D.T.near" "D.P.gold" "D.P.http"
## [13] "D.T.fair" "D.T.profession" "D.npnct02.log" "D.npnct04.log"
## [17] "D.npnct09.log" "D.npnct17.log" "D.npnct18.log" "D.npnct19.log"
## [21] "D.npnct20.log" "D.npnct21.log" "D.npnct22.log" "D.npnct23.log"
## [25] "D.npnct25.log" "D.npnct26.log" "D.npnct27.log" "D.npnct28.log"
## [29] "D.npnct29.log" "D.npnct30.log" "sold"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2657 60print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 860 59print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 860 59print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 1797 59print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 1797 59# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 116.123 116.617 0.494
## 10 fit.models 7 0 116.618 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -129.53 -108.00 -29.01 69.04 547.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 127.444 4.474 28.487 <2e-16 ***
## .rnorm -1.111 4.462 -0.249 0.803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 131.2 on 858 degrees of freedom
## Multiple R-squared: 7.226e-05, Adjusted R-squared: -0.001093
## F-statistic: 0.06201 on 1 and 858 DF, p-value: 0.8034
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.48
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.003 7.226357e-05 131.0399 0.0001316983
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 212.9262 -0.001093153if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"## Loading required package: rpart## Fitting cp = 0.229 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.229411 0 1
##
## Node number 1: 860 observations
## mean=127.4371, MSE=17172.71
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530 127.4371 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, prdline.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.618 0.011
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0 131.0447 0 212.9402ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 2.294110e-01 0 1.0000000
## 2 8.271687e-02 1 0.7705890
## 3 8.034499e-02 2 0.6878721
## 4 4.511997e-02 3 0.6075271
## 5 2.018735e-02 4 0.5624072
## 6 2.004163e-02 5 0.5422198
## 7 6.459770e-03 6 0.5221782
## 8 3.727090e-03 7 0.5157184
## 9 2.115310e-03 8 0.5119913
## 10 1.441852e-03 9 0.5098760
## 11 6.512440e-04 10 0.5084341
## 12 8.495501e-05 11 0.5077829
## 13 7.035010e-05 12 0.5076979
## 14 0.000000e+00 13 0.5076276
##
## Variable importance
## biddable prdline.my.fctriPadAir
## 47 33
## prdline.my.fctriPadmini 2+ prdline.my.fctriPad 1
## 13 4
## prdline.my.fctriPad 3+ prdline.my.fctriPad 2
## 1 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.05938979, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04145277, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.03061258, (0 missing)
##
## Node number 2: 640 observations, complexity param=0.08034499
## mean=90.63711, MSE=11139.65
## left son=4 (527 obs) right son=5 (113 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.166435000, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.041416060, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.026974120, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.021240580, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.008701147, (0 missing)
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.28736310, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.17624070, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.09007232, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.04780316, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.01981494, (0 missing)
##
## Node number 4: 527 observations, complexity param=0.02018735
## mean=70.69863, MSE=5602.216
## left son=8 (474 obs) right son=9 (53 obs)
## Primary splits:
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.100982500, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.038684240, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.015093930, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.014567220, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.000390718, (0 missing)
##
## Node number 5: 113 observations
## mean=183.6245, MSE=26463.98
##
## Node number 6: 183 observations, complexity param=0.04511997
## mean=200.9851, MSE=13424.58
## left son=12 (156 obs) right son=13 (27 obs)
## Primary splits:
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.271240400, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.189593800, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.025460260, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.004209899, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.002555074, (0 missing)
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## Node number 8: 474 observations, complexity param=0.00645977
## mean=62.74525, MSE=4145.721
## left son=16 (365 obs) right son=17 (109 obs)
## Primary splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.048548510, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.033084020, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.006462783, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.001514746, (0 missing)
##
## Node number 9: 53 observations
## mean=141.8289, MSE=13003
##
## Node number 12: 156 observations, complexity param=0.02004163
## mean=175.8809, MSE=9753.424
## left son=24 (29 obs) right son=25 (127 obs)
## Primary splits:
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.194530900, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.049943280, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.005997696, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.004713566, (0 missing)
##
## Node number 13: 27 observations
## mean=346.0319, MSE=9955.85
##
## Node number 16: 365 observations, complexity param=0.00211531
## mean=54.99252, MSE=3182.492
## left son=32 (96 obs) right son=33 (269 obs)
## Primary splits:
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.026893730, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.018654610, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.000280386, (0 missing)
##
## Node number 17: 109 observations
## mean=88.70624, MSE=6495.969
##
## Node number 24: 29 observations
## mean=84.7269, MSE=1785.663
##
## Node number 25: 127 observations, complexity param=0.00372709
## mean=196.6956, MSE=9242.241
## left son=50 (32 obs) right son=51 (95 obs)
## Primary splits:
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.046894960, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.016244160, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.002513227, (0 missing)
##
## Node number 32: 96 observations
## mean=39.50615, MSE=1380.205
##
## Node number 33: 269 observations, complexity param=0.000651244
## mean=60.51926, MSE=3709.554
## left son=66 (115 obs) right son=67 (154 obs)
## Primary splits:
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.009638447, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.007920794, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadmini < 0.5 to the left, agree=0.796, adj=0.522, (0 split)
##
## Node number 50: 32 observations
## mean=160.825, MSE=2119.278
##
## Node number 51: 95 observations, complexity param=0.001441852
## mean=208.7783, MSE=11062.15
## left son=102 (34 obs) right son=103 (61 obs)
## Primary splits:
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.020262580, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.003018897, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, agree=0.737, adj=0.265, (0 split)
##
## Node number 66: 115 observations
## mean=53.59974, MSE=2373.183
##
## Node number 67: 154 observations, complexity param=7.03501e-05
## mean=65.68643, MSE=4645.039
## left son=134 (55 obs) right son=135 (99 obs)
## Primary splits:
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.001452419, (0 missing)
##
## Node number 102: 34 observations
## mean=188.7247, MSE=5781.186
##
## Node number 103: 61 observations, complexity param=8.495501e-05
## mean=219.9557, MSE=13656.55
## left son=206 (36 obs) right son=207 (25 obs)
## Primary splits:
## prdline.my.fctriPad 3+ < 0.5 to the right, improve=0.001506105, (0 missing)
##
## Node number 134: 55 observations
## mean=62.20164, MSE=6081.402
##
## Node number 135: 99 observations
## mean=67.62242, MSE=3836.565
##
## Node number 206: 36 observations
## mean=216.1764, MSE=4876.6
##
## Node number 207: 25 observations
## mean=225.398, MSE=26249.5
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.00 127.43710
## 2) biddable>=0.5 640 7129375.00 90.63711
## 4) prdline.my.fctriPadAir< 0.5 527 2952368.00 70.69863
## 8) prdline.my.fctriPadmini 2+< 0.5 474 1965072.00 62.74525
## 16) prdline.my.fctriPad 3+< 0.5 365 1161610.00 54.99252
## 32) prdline.my.fctriPad 1>=0.5 96 132499.70 39.50615 *
## 33) prdline.my.fctriPad 1< 0.5 269 997870.00 60.51926
## 66) prdline.my.fctriPad 2>=0.5 115 272916.00 53.59974 *
## 67) prdline.my.fctriPad 2< 0.5 154 715336.10 65.68643
## 134) prdline.my.fctriPadmini< 0.5 55 334477.10 62.20164 *
## 135) prdline.my.fctriPadmini>=0.5 99 379820.00 67.62242 *
## 17) prdline.my.fctriPad 3+>=0.5 109 708060.70 88.70624 *
## 9) prdline.my.fctriPadmini 2+>=0.5 53 689158.80 141.82890 *
## 5) prdline.my.fctriPadAir>=0.5 113 2990430.00 183.62450 *
## 3) biddable< 0.5 220 4251091.00 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.00 200.98510
## 12) prdline.my.fctriPadmini 2+< 0.5 156 1521534.00 175.88090
## 24) prdline.my.fctriPad 1>=0.5 29 51784.21 84.72690 *
## 25) prdline.my.fctriPad 1< 0.5 127 1173765.00 196.69560
## 50) prdline.my.fctriPad 2>=0.5 32 67816.89 160.82500 *
## 51) prdline.my.fctriPad 2< 0.5 95 1050904.00 208.77830
## 102) prdline.my.fctriPadmini>=0.5 34 196560.30 188.72470 *
## 103) prdline.my.fctriPadmini< 0.5 61 833049.70 219.95570
## 206) prdline.my.fctriPad 3+>=0.5 36 175557.60 216.17640 *
## 207) prdline.my.fctriPad 3+< 0.5 25 656237.40 225.39800 *
## 13) prdline.my.fctriPadmini 2+>=0.5 27 268808.00 346.03190 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.80 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, prdline.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.49 0.011
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.4923724 93.3667 0.5489639 143.009if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0803 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.0000000
## 2 0.08271687 1 0.7705890
## 3 0.08034499 2 0.6878721
##
## Variable importance
## biddable prdline.my.fctriPadAir
## 73 27
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.05938979, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04145277, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.03061258, (0 missing)
##
## Node number 2: 640 observations
## mean=90.63711, MSE=11139.65
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.28736310, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.17624070, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.09007232, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.04780316, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.01981494, (0 missing)
##
## Node number 6: 183 observations
## mean=200.9851, MSE=13424.58
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.0 127.43710
## 2) biddable>=0.5 640 7129375.0 90.63711 *
## 3) biddable< 0.5 220 4251091.0 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.0 200.98510 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.8 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, prdline.my.fctr 3
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.005 0.011 0.3121279
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 111.8385 0.450545 157.8425 0.2750573 3.592112
## max.RsquaredSD.fit
## 1 0.04148092# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: biddable, prdline.my.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -283.21 -61.33 -5.70 47.57 447.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 209.168 12.015 17.409 < 2e-16 ***
## biddable -139.589 7.591 -18.389 < 2e-16 ***
## `prdline.my.fctriPad 1` -51.966 13.873 -3.746 0.000192 ***
## `prdline.my.fctriPad 2` -23.024 13.468 -1.710 0.087716 .
## `prdline.my.fctriPad 3+` 16.118 13.490 1.195 0.232495
## prdline.my.fctriPadAir 133.039 13.411 9.920 < 2e-16 ***
## prdline.my.fctriPadmini -6.682 13.703 -0.488 0.625913
## `prdline.my.fctriPadmini 2+` 94.057 15.307 6.145 1.23e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 96.8 on 852 degrees of freedom
## Multiple R-squared: 0.4594, Adjusted R-squared: 0.455
## F-statistic: 103.4 on 7 and 852 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.lm lm biddable, prdline.my.fctr 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.938 0.006 0.459417
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 97.12892 0.5186351 147.7389 0.4549756 0.452455
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.422758 0.04130826if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.lm"
## [1] " indep_vars: biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -283.45 -57.03 -3.11 48.35 436.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 212.39 11.98 17.727 < 2e-16
## biddable -221.70 69.89 -3.172 0.00157
## `prdline.my.fctriPad 1` -56.16 13.88 -4.046 5.68e-05
## `prdline.my.fctriPad 2` -25.40 13.44 -1.890 0.05910
## `prdline.my.fctriPad 3+` 13.07 13.44 0.973 0.33095
## prdline.my.fctriPadAir 130.06 13.37 9.727 < 2e-16
## prdline.my.fctriPadmini -11.04 13.75 -0.803 0.42225
## `prdline.my.fctriPadmini 2+` 88.72 15.27 5.811 8.82e-09
## `biddable:D.TfIdf.sum.post.stop` -30.97 22.16 -1.398 0.16262
## `biddable:D.npnct06.log` 63.76 44.60 1.430 0.15320
## `biddable:D.npnct03.log` -48.99 54.16 -0.905 0.36598
## `biddable:D.terms.n.post.stem` -15.68 10.34 -1.516 0.12993
## `biddable:D.nuppr.log` -17.32 38.11 -0.454 0.64968
## `biddable:D.nwrds.unq.log` 149.73 98.95 1.513 0.13062
## `biddable:D.npnct24.log` -100.25 131.22 -0.764 0.44511
## `biddable:D.ratio.nstopwrds.nwrds` 92.72 69.43 1.336 0.18207
## `biddable:D.TfIdf.sum.post.stem` 28.09 23.35 1.203 0.22924
##
## (Intercept) ***
## biddable **
## `prdline.my.fctriPad 1` ***
## `prdline.my.fctriPad 2` .
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` ***
## `biddable:D.TfIdf.sum.post.stop`
## `biddable:D.npnct06.log`
## `biddable:D.npnct03.log`
## `biddable:D.terms.n.post.stem`
## `biddable:D.nuppr.log`
## `biddable:D.nwrds.unq.log`
## `biddable:D.npnct24.log`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.TfIdf.sum.post.stem`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 96.03 on 843 degrees of freedom
## Multiple R-squared: 0.4737, Adjusted R-squared: 0.4637
## F-statistic: 47.42 on 16 and 843 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Interact.High.cor.Y.lm lm
## feats
## 1 biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.982 0.009
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.4736677 96.61314 0.5213162 147.3269 0.463678
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4581469 3.790992 0.04312606# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -294.46 -44.33 -1.00 47.94 330.60
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 2.513e+02 1.894e+02
## `prdline.my.fctriPad 1` -6.487e+01 1.686e+01
## `prdline.my.fctriPad 2` -3.079e+01 1.635e+01
## `prdline.my.fctriPad 3+` 4.123e+00 1.632e+01
## prdline.my.fctriPadAir 8.839e+01 1.675e+01
## prdline.my.fctriPadmini -1.574e+01 1.660e+01
## `prdline.my.fctriPadmini 2+` 3.929e+01 1.784e+01
## `condition.fctrFor parts or not working` -4.910e+01 1.222e+01
## `condition.fctrManufacturer refurbished` -9.803e+00 2.399e+01
## condition.fctrNew 6.930e+01 1.157e+01
## `condition.fctrNew other (see details)` 4.905e+01 1.600e+01
## `condition.fctrSeller refurbished` -2.759e+01 1.578e+01
## D.TfIdf.sum.stem.stop.Ratio 1.739e+02 1.108e+02
## color.fctrGold -4.799e+00 2.208e+01
## `color.fctrSpace Gray` 1.485e+01 1.230e+01
## color.fctrUnknown -4.662e+00 8.227e+00
## color.fctrWhite 1.806e+01 9.241e+00
## carrier.fctrNone 4.122e+01 2.357e+01
## carrier.fctrOther 1.154e+02 5.379e+01
## carrier.fctrSprint -3.446e+01 2.692e+01
## `carrier.fctrT-Mobile` 6.033e+00 3.166e+01
## carrier.fctrUnknown 1.083e+01 1.640e+01
## carrier.fctrVerizon 9.186e+00 1.463e+01
## storage.fctr16 -1.357e+02 1.978e+01
## storage.fctr32 -1.208e+02 2.064e+01
## storage.fctr64 -8.638e+01 2.040e+01
## storage.fctrUnknown -8.871e+01 2.674e+01
## D.npnct14.log -1.617e+01 3.752e+01
## cellular.fctr1 4.130e+01 2.145e+01
## cellular.fctrUnknown NA NA
## D.terms.n.stem.stop.Ratio -1.081e+02 1.649e+02
## D.ndgts.log 8.921e+00 1.493e+01
## .rnorm -1.234e+00 3.009e+00
## idseq.my -1.263e-02 7.406e-03
## D.npnct08.log 6.759e+00 2.321e+01
## D.npnct05.log -8.876e+01 7.725e+01
## D.npnct15.log -3.260e+01 3.114e+01
## D.npnct01.log 9.881e+00 1.927e+01
## D.npnct12.log 1.878e+01 2.377e+01
## D.npnct03.log 2.592e+00 3.922e+01
## D.npnct11.log -2.080e+01 1.153e+01
## D.npnct13.log -1.621e+01 1.022e+01
## D.TfIdf.sum.post.stop 4.971e+00 2.294e+00
## D.ratio.sum.TfIdf.nwrds -3.226e+01 6.830e+00
## biddable -1.382e+02 7.387e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 8.563e-02 3.250e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 2.028e+01 4.074e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 3.938e+01 5.468e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -4.323e+01 5.628e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.765e+01 3.122e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 5.171e+00 2.338e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 6.279e-01 4.089e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -3.575e+01 4.069e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -9.245e+01 4.509e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA NA
## t value Pr(>|t|)
## (Intercept) 1.327 0.184871
## `prdline.my.fctriPad 1` -3.847 0.000129 ***
## `prdline.my.fctriPad 2` -1.884 0.059949 .
## `prdline.my.fctriPad 3+` 0.253 0.800577
## prdline.my.fctriPadAir 5.276 1.69e-07 ***
## prdline.my.fctriPadmini -0.948 0.343339
## `prdline.my.fctriPadmini 2+` 2.203 0.027870 *
## `condition.fctrFor parts or not working` -4.018 6.41e-05 ***
## `condition.fctrManufacturer refurbished` -0.409 0.682926
## condition.fctrNew 5.988 3.19e-09 ***
## `condition.fctrNew other (see details)` 3.066 0.002239 **
## `condition.fctrSeller refurbished` -1.748 0.080783 .
## D.TfIdf.sum.stem.stop.Ratio 1.569 0.116956
## color.fctrGold -0.217 0.828044
## `color.fctrSpace Gray` 1.208 0.227551
## color.fctrUnknown -0.567 0.571076
## color.fctrWhite 1.955 0.050951 .
## carrier.fctrNone 1.749 0.080754 .
## carrier.fctrOther 2.145 0.032277 *
## carrier.fctrSprint -1.280 0.200909
## `carrier.fctrT-Mobile` 0.191 0.848897
## carrier.fctrUnknown 0.661 0.509086
## carrier.fctrVerizon 0.628 0.530174
## storage.fctr16 -6.859 1.38e-11 ***
## storage.fctr32 -5.855 6.92e-09 ***
## storage.fctr64 -4.234 2.56e-05 ***
## storage.fctrUnknown -3.318 0.000948 ***
## D.npnct14.log -0.431 0.666531
## cellular.fctr1 1.925 0.054551 .
## cellular.fctrUnknown NA NA
## D.terms.n.stem.stop.Ratio -0.655 0.512371
## D.ndgts.log 0.597 0.550347
## .rnorm -0.410 0.681816
## idseq.my -1.705 0.088559 .
## D.npnct08.log 0.291 0.770947
## D.npnct05.log -1.149 0.250931
## D.npnct15.log -1.047 0.295400
## D.npnct01.log 0.513 0.608355
## D.npnct12.log 0.790 0.429769
## D.npnct03.log 0.066 0.947315
## D.npnct11.log -1.804 0.071671 .
## D.npnct13.log -1.587 0.113007
## D.TfIdf.sum.post.stop 2.167 0.030513 *
## D.ratio.sum.TfIdf.nwrds -4.723 2.74e-06 ***
## biddable -18.711 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.003 0.997899
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.498 0.618775
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.720 0.471594
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.768 0.442614
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.565 0.571925
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.221 0.824988
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.015 0.987752
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.879 0.379839
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.050 0.040657 *
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 86.02 on 807 degrees of freedom
## Multiple R-squared: 0.5956, Adjusted R-squared: 0.5696
## F-statistic: 22.86 on 52 and 807 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.033 0.027
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.5956499 91.52464 0.615721 132.0022 0.5695952
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5176304 1.527531 0.01733469rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 116.618 132.159 15.541
## 11 fit.models 7 1 132.159 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 134.277 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
for (model_id_pfx in c("All.X", "All.Interact.X")) {
#model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
if (model_id_pfx == "All.Interact.X") {
# !_sp
# interact_vars_vctr <- c(
# "idseq.my", "D.ratio.sum.TfIdf.nwrds", "D.TfIdf.sum.stem.stop.Ratio",
# "D.npnct15.log", "D.npnct03.log")
###
# _sp only
interact_vars_vctr <- c(
"D.nchrs.log", "D.TfIdf.sum.stem.stop.Ratio",
"D.npnct16.log", "D.npnct01.log", "D.nstopwrds.log", "D.npnct08.log",
"D.terms.n.post.stop", "D.terms.n.post.stem",
"biddable", "condition.fctr",
# "cellular.fctr", "carrier.fctr",
"color.fctr", "storage.fctr", "idseq.my")
###
indep_vars_vctr <- union(setdiff(indep_vars_vctr, interact_vars_vctr),
paste(glb_category_var, interact_vars_vctr, sep=".fctr*"))
# !_sp
# indep_vars_vctr <- union(setdiff(indep_vars_vctr,
# c("startprice.diff", "biddable", "cellular.fctr", "carrier.fctr")),
# c("startprice.diff*biddable", "cellular.fctr*carrier.fctr"))
# _sp
indep_vars_vctr <- union(setdiff(indep_vars_vctr,
c("cellular.fctr", "carrier.fctr")),
c("cellular.fctr*carrier.fctr"))
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# mydsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 134.277 134.286 0.01
## 2 fit.models_1_lm 2 0 134.287 NA NA
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -282.32 -46.15 -0.74 48.54 328.38
##
## Coefficients: (8 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -7.681e+04 8.360e+04
## `prdline.my.fctriPad 1` -7.012e+01 1.700e+01
## `prdline.my.fctriPad 2` -3.358e+01 1.647e+01
## `prdline.my.fctriPad 3+` -1.296e+00 1.646e+01
## prdline.my.fctriPadAir 8.578e+01 1.683e+01
## prdline.my.fctriPadmini -1.829e+01 1.664e+01
## `prdline.my.fctriPadmini 2+` 3.746e+01 1.793e+01
## `condition.fctrFor parts or not working` -5.107e+01 1.237e+01
## `condition.fctrManufacturer refurbished` -1.162e+01 2.393e+01
## condition.fctrNew 6.750e+01 1.161e+01
## `condition.fctrNew other (see details)` 5.420e+01 1.631e+01
## `condition.fctrSeller refurbished` -3.092e+01 1.631e+01
## D.ratio.nstopwrds.nwrds -2.287e+01 2.446e+02
## D.TfIdf.sum.stem.stop.Ratio 5.979e+02 5.862e+02
## color.fctrGold -2.275e+00 2.222e+01
## `color.fctrSpace Gray` 1.500e+01 1.228e+01
## color.fctrUnknown -4.481e+00 8.250e+00
## color.fctrWhite 1.946e+01 9.269e+00
## carrier.fctrNone 4.252e+01 2.361e+01
## carrier.fctrOther 1.100e+02 5.451e+01
## carrier.fctrSprint -3.582e+01 2.712e+01
## `carrier.fctrT-Mobile` 1.380e+01 3.171e+01
## carrier.fctrUnknown 1.276e+01 1.638e+01
## carrier.fctrVerizon 1.034e+01 1.464e+01
## storage.fctr16 -1.364e+02 1.972e+01
## storage.fctr32 -1.231e+02 2.059e+01
## storage.fctr64 -8.853e+01 2.034e+01
## storage.fctrUnknown -9.270e+01 2.675e+01
## D.npnct14.log -4.007e+00 3.836e+01
## cellular.fctr1 4.136e+01 2.146e+01
## cellular.fctrUnknown NA NA
## D.terms.n.stem.stop.Ratio 7.655e+04 8.363e+04
## D.ndgts.log 6.598e-01 1.946e+01
## .rnorm -7.232e-01 3.022e+00
## idseq.my -1.479e-02 7.433e-03
## D.npnct08.log 1.333e+01 2.355e+01
## D.npnct05.log -3.631e+01 7.891e+01
## D.npnct15.log -1.757e+01 3.198e+01
## D.npnct01.log 3.179e+01 2.224e+01
## D.npnct16.log 4.267e+01 6.701e+01
## D.npnct12.log 1.119e+01 2.484e+01
## D.npnct06.log 3.994e+01 7.799e+01
## D.npnct03.log -2.816e+01 5.455e+01
## D.nstopwrds.log -6.425e+01 7.413e+01
## D.npnct11.log -2.461e+01 1.303e+01
## D.npnct13.log -1.678e+01 1.328e+01
## D.terms.n.post.stop -6.604e+02 5.710e+02
## D.terms.n.post.stem 6.616e+02 5.725e+02
## D.nwrds.log 2.179e+02 8.995e+01
## D.terms.n.post.stop.log 8.706e+04 9.366e+04
## D.nwrds.unq.log -8.713e+04 9.368e+04
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -3.520e+02 1.688e+02
## D.nuppr.log 3.104e+02 1.477e+02
## D.npnct24.log -8.568e+01 2.091e+02
## D.TfIdf.sum.post.stem -7.571e+01 9.283e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 7.289e+01 8.866e+01
## D.ratio.sum.TfIdf.nwrds -1.116e+01 1.743e+01
## biddable -1.391e+02 7.418e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` -2.122e+01 3.468e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.720e+01 4.089e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.643e+01 5.523e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -7.570e+01 5.727e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.897e+01 3.168e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` -2.142e+00 2.383e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -4.778e-01 4.093e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.756e+01 4.152e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.244e+02 4.724e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA NA
## t value Pr(>|t|)
## (Intercept) -0.919 0.358532
## `prdline.my.fctriPad 1` -4.126 4.09e-05 ***
## `prdline.my.fctriPad 2` -2.039 0.041769 *
## `prdline.my.fctriPad 3+` -0.079 0.937269
## prdline.my.fctriPadAir 5.098 4.31e-07 ***
## prdline.my.fctriPadmini -1.099 0.272186
## `prdline.my.fctriPadmini 2+` 2.089 0.037011 *
## `condition.fctrFor parts or not working` -4.128 4.05e-05 ***
## `condition.fctrManufacturer refurbished` -0.486 0.627332
## condition.fctrNew 5.816 8.73e-09 ***
## `condition.fctrNew other (see details)` 3.323 0.000931 ***
## `condition.fctrSeller refurbished` -1.896 0.058367 .
## D.ratio.nstopwrds.nwrds -0.094 0.925520
## D.TfIdf.sum.stem.stop.Ratio 1.020 0.308037
## color.fctrGold -0.102 0.918473
## `color.fctrSpace Gray` 1.221 0.222384
## color.fctrUnknown -0.543 0.587211
## color.fctrWhite 2.100 0.036074 *
## carrier.fctrNone 1.801 0.072041 .
## carrier.fctrOther 2.017 0.044007 *
## carrier.fctrSprint -1.321 0.186839
## `carrier.fctrT-Mobile` 0.435 0.663643
## carrier.fctrUnknown 0.779 0.436071
## carrier.fctrVerizon 0.706 0.480096
## storage.fctr16 -6.915 9.61e-12 ***
## storage.fctr32 -5.978 3.42e-09 ***
## storage.fctr64 -4.352 1.53e-05 ***
## storage.fctrUnknown -3.466 0.000557 ***
## D.npnct14.log -0.104 0.916837
## cellular.fctr1 1.928 0.054246 .
## cellular.fctrUnknown NA NA
## D.terms.n.stem.stop.Ratio 0.915 0.360239
## D.ndgts.log 0.034 0.972964
## .rnorm -0.239 0.810915
## idseq.my -1.990 0.046928 *
## D.npnct08.log 0.566 0.571370
## D.npnct05.log -0.460 0.645560
## D.npnct15.log -0.549 0.582891
## D.npnct01.log 1.429 0.153263
## D.npnct16.log 0.637 0.524469
## D.npnct12.log 0.450 0.652530
## D.npnct06.log 0.512 0.608677
## D.npnct03.log -0.516 0.605899
## D.nstopwrds.log -0.867 0.386350
## D.npnct11.log -1.889 0.059249 .
## D.npnct13.log -1.263 0.206820
## D.terms.n.post.stop -1.157 0.247801
## D.terms.n.post.stem 1.156 0.248150
## D.nwrds.log 2.423 0.015620 *
## D.terms.n.post.stop.log 0.930 0.352899
## D.nwrds.unq.log -0.930 0.352624
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -2.086 0.037286 *
## D.nuppr.log 2.102 0.035864 *
## D.npnct24.log -0.410 0.682067
## D.TfIdf.sum.post.stem -0.816 0.414976
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.822 0.411234
## D.ratio.sum.TfIdf.nwrds -0.640 0.522240
## biddable -18.753 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` -0.612 0.540908
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.421 0.674103
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.297 0.766216
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.322 0.186601
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.599 0.549496
## `prdline.my.fctriPadmini:.clusterid.fctr2` -0.090 0.928400
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.012 0.990689
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.386 0.166076
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.633 0.008617 **
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 85.55 on 794 degrees of freedom
## Multiple R-squared: 0.6066, Adjusted R-squared: 0.5743
## F-statistic: 18.83 on 65 and 794 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.035 0.033
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.606553 95.96146 0.5925191 135.9288 0.5743438
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4835691 4.088978 0.03215851
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_lm 2 0 134.287 137.106 2.819
## 3 fit.models_1_glm 3 0 137.107 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -282.32 -46.15 -0.74 48.54 328.38
##
## Coefficients: (8 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -7.681e+04 8.360e+04
## `prdline.my.fctriPad 1` -7.012e+01 1.700e+01
## `prdline.my.fctriPad 2` -3.358e+01 1.647e+01
## `prdline.my.fctriPad 3+` -1.296e+00 1.646e+01
## prdline.my.fctriPadAir 8.578e+01 1.683e+01
## prdline.my.fctriPadmini -1.829e+01 1.664e+01
## `prdline.my.fctriPadmini 2+` 3.746e+01 1.793e+01
## `condition.fctrFor parts or not working` -5.107e+01 1.237e+01
## `condition.fctrManufacturer refurbished` -1.162e+01 2.393e+01
## condition.fctrNew 6.750e+01 1.161e+01
## `condition.fctrNew other (see details)` 5.420e+01 1.631e+01
## `condition.fctrSeller refurbished` -3.092e+01 1.631e+01
## D.ratio.nstopwrds.nwrds -2.287e+01 2.446e+02
## D.TfIdf.sum.stem.stop.Ratio 5.979e+02 5.862e+02
## color.fctrGold -2.275e+00 2.222e+01
## `color.fctrSpace Gray` 1.500e+01 1.228e+01
## color.fctrUnknown -4.481e+00 8.250e+00
## color.fctrWhite 1.946e+01 9.269e+00
## carrier.fctrNone 4.252e+01 2.361e+01
## carrier.fctrOther 1.100e+02 5.451e+01
## carrier.fctrSprint -3.582e+01 2.712e+01
## `carrier.fctrT-Mobile` 1.380e+01 3.171e+01
## carrier.fctrUnknown 1.276e+01 1.638e+01
## carrier.fctrVerizon 1.034e+01 1.464e+01
## storage.fctr16 -1.364e+02 1.972e+01
## storage.fctr32 -1.231e+02 2.059e+01
## storage.fctr64 -8.853e+01 2.034e+01
## storage.fctrUnknown -9.270e+01 2.675e+01
## D.npnct14.log -4.007e+00 3.836e+01
## cellular.fctr1 4.136e+01 2.146e+01
## cellular.fctrUnknown NA NA
## D.terms.n.stem.stop.Ratio 7.655e+04 8.363e+04
## D.ndgts.log 6.598e-01 1.946e+01
## .rnorm -7.232e-01 3.022e+00
## idseq.my -1.479e-02 7.433e-03
## D.npnct08.log 1.333e+01 2.355e+01
## D.npnct05.log -3.631e+01 7.891e+01
## D.npnct15.log -1.757e+01 3.198e+01
## D.npnct01.log 3.179e+01 2.224e+01
## D.npnct16.log 4.267e+01 6.701e+01
## D.npnct12.log 1.119e+01 2.484e+01
## D.npnct06.log 3.994e+01 7.799e+01
## D.npnct03.log -2.816e+01 5.455e+01
## D.nstopwrds.log -6.425e+01 7.413e+01
## D.npnct11.log -2.461e+01 1.303e+01
## D.npnct13.log -1.678e+01 1.328e+01
## D.terms.n.post.stop -6.604e+02 5.710e+02
## D.terms.n.post.stem 6.616e+02 5.725e+02
## D.nwrds.log 2.179e+02 8.995e+01
## D.terms.n.post.stop.log 8.706e+04 9.366e+04
## D.nwrds.unq.log -8.713e+04 9.368e+04
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -3.520e+02 1.688e+02
## D.nuppr.log 3.104e+02 1.477e+02
## D.npnct24.log -8.568e+01 2.091e+02
## D.TfIdf.sum.post.stem -7.571e+01 9.283e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 7.289e+01 8.866e+01
## D.ratio.sum.TfIdf.nwrds -1.116e+01 1.743e+01
## biddable -1.391e+02 7.418e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` -2.122e+01 3.468e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.720e+01 4.089e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.643e+01 5.523e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -7.570e+01 5.727e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.897e+01 3.168e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` -2.142e+00 2.383e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -4.778e-01 4.093e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.756e+01 4.152e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.244e+02 4.724e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA NA
## t value Pr(>|t|)
## (Intercept) -0.919 0.358532
## `prdline.my.fctriPad 1` -4.126 4.09e-05 ***
## `prdline.my.fctriPad 2` -2.039 0.041769 *
## `prdline.my.fctriPad 3+` -0.079 0.937269
## prdline.my.fctriPadAir 5.098 4.31e-07 ***
## prdline.my.fctriPadmini -1.099 0.272186
## `prdline.my.fctriPadmini 2+` 2.089 0.037011 *
## `condition.fctrFor parts or not working` -4.128 4.05e-05 ***
## `condition.fctrManufacturer refurbished` -0.486 0.627332
## condition.fctrNew 5.816 8.73e-09 ***
## `condition.fctrNew other (see details)` 3.323 0.000931 ***
## `condition.fctrSeller refurbished` -1.896 0.058367 .
## D.ratio.nstopwrds.nwrds -0.094 0.925520
## D.TfIdf.sum.stem.stop.Ratio 1.020 0.308037
## color.fctrGold -0.102 0.918473
## `color.fctrSpace Gray` 1.221 0.222384
## color.fctrUnknown -0.543 0.587211
## color.fctrWhite 2.100 0.036074 *
## carrier.fctrNone 1.801 0.072041 .
## carrier.fctrOther 2.017 0.044007 *
## carrier.fctrSprint -1.321 0.186839
## `carrier.fctrT-Mobile` 0.435 0.663643
## carrier.fctrUnknown 0.779 0.436071
## carrier.fctrVerizon 0.706 0.480096
## storage.fctr16 -6.915 9.61e-12 ***
## storage.fctr32 -5.978 3.42e-09 ***
## storage.fctr64 -4.352 1.53e-05 ***
## storage.fctrUnknown -3.466 0.000557 ***
## D.npnct14.log -0.104 0.916837
## cellular.fctr1 1.928 0.054246 .
## cellular.fctrUnknown NA NA
## D.terms.n.stem.stop.Ratio 0.915 0.360239
## D.ndgts.log 0.034 0.972964
## .rnorm -0.239 0.810915
## idseq.my -1.990 0.046928 *
## D.npnct08.log 0.566 0.571370
## D.npnct05.log -0.460 0.645560
## D.npnct15.log -0.549 0.582891
## D.npnct01.log 1.429 0.153263
## D.npnct16.log 0.637 0.524469
## D.npnct12.log 0.450 0.652530
## D.npnct06.log 0.512 0.608677
## D.npnct03.log -0.516 0.605899
## D.nstopwrds.log -0.867 0.386350
## D.npnct11.log -1.889 0.059249 .
## D.npnct13.log -1.263 0.206820
## D.terms.n.post.stop -1.157 0.247801
## D.terms.n.post.stem 1.156 0.248150
## D.nwrds.log 2.423 0.015620 *
## D.terms.n.post.stop.log 0.930 0.352899
## D.nwrds.unq.log -0.930 0.352624
## D.terms.n.post.stem.log NA NA
## D.nchrs.log -2.086 0.037286 *
## D.nuppr.log 2.102 0.035864 *
## D.npnct24.log -0.410 0.682067
## D.TfIdf.sum.post.stem -0.816 0.414976
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.822 0.411234
## D.ratio.sum.TfIdf.nwrds -0.640 0.522240
## biddable -18.753 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` -0.612 0.540908
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.421 0.674103
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.297 0.766216
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.322 0.186601
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.599 0.549496
## `prdline.my.fctriPadmini:.clusterid.fctr2` -0.090 0.928400
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.012 0.990689
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.386 0.166076
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.633 0.008617 **
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7318.179)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5810634 on 794 degrees of freedom
## AIC: 10158
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## model_id model_method
## 1 All.X.glm glm
## feats
## 1 prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.102 0.047
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.606553 95.96146 0.5925191 135.9288 10158.28
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4835691 4.088978 0.03215851
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_glm 3 0 137.107 140.084 2.977
## 4 fit.models_1_bayesglm 4 0 140.085 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loading required package: lme4
##
## Attaching package: 'lme4'
##
## The following object is masked from 'package:nlme':
##
## lmList
##
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -282.25 -45.91 -1.20 47.72 327.13
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 3.935e+02 7.692e+02
## `prdline.my.fctriPad 1` -7.056e+01 1.704e+01
## `prdline.my.fctriPad 2` -3.424e+01 1.649e+01
## `prdline.my.fctriPad 3+` -1.082e+00 1.651e+01
## prdline.my.fctriPadAir 8.551e+01 1.688e+01
## prdline.my.fctriPadmini -1.937e+01 1.667e+01
## `prdline.my.fctriPadmini 2+` 3.719e+01 1.797e+01
## `condition.fctrFor parts or not working` -5.217e+01 1.240e+01
## `condition.fctrManufacturer refurbished` -1.201e+01 2.403e+01
## condition.fctrNew 6.703e+01 1.166e+01
## `condition.fctrNew other (see details)` 5.316e+01 1.638e+01
## `condition.fctrSeller refurbished` -3.087e+01 1.614e+01
## D.ratio.nstopwrds.nwrds 2.696e+00 2.126e+02
## D.TfIdf.sum.stem.stop.Ratio 2.962e+02 4.116e+02
## color.fctrGold -2.357e+00 2.228e+01
## `color.fctrSpace Gray` 1.544e+01 1.234e+01
## color.fctrUnknown -4.855e+00 8.289e+00
## color.fctrWhite 1.977e+01 9.294e+00
## carrier.fctrNone 1.459e+01 2.956e+02
## carrier.fctrOther 1.095e+02 5.430e+01
## carrier.fctrSprint -3.591e+01 2.721e+01
## `carrier.fctrT-Mobile` 1.247e+01 3.180e+01
## carrier.fctrUnknown 1.271e+01 1.644e+01
## carrier.fctrVerizon 9.214e+00 1.468e+01
## storage.fctr16 -1.357e+02 1.976e+01
## storage.fctr32 -1.223e+02 2.064e+01
## storage.fctr64 -8.768e+01 2.040e+01
## storage.fctrUnknown -9.190e+01 2.680e+01
## D.npnct14.log -4.246e+00 3.825e+01
## cellular.fctr1 1.341e+01 2.955e+02
## cellular.fctrUnknown -2.803e+01 2.958e+02
## D.terms.n.stem.stop.Ratio -3.421e+02 5.408e+02
## D.ndgts.log 6.195e-01 1.903e+01
## .rnorm -8.536e-01 3.035e+00
## idseq.my -1.408e-02 7.459e-03
## D.npnct08.log 1.177e+01 2.360e+01
## D.npnct05.log -3.913e+01 7.804e+01
## D.npnct15.log -1.939e+01 3.196e+01
## D.npnct01.log 3.012e+01 2.191e+01
## D.npnct16.log 4.307e+01 6.588e+01
## D.npnct12.log 1.032e+01 2.472e+01
## D.npnct06.log 3.576e+01 7.643e+01
## D.npnct03.log -2.970e+01 5.398e+01
## D.nstopwrds.log -6.769e+01 6.618e+01
## D.npnct11.log -2.536e+01 1.296e+01
## D.npnct13.log -1.875e+01 1.312e+01
## D.terms.n.post.stop -6.123e+01 6.784e+01
## D.terms.n.post.stem 6.137e+01 6.898e+01
## D.nwrds.log 2.084e+02 8.554e+01
## D.terms.n.post.stop.log 1.392e+02 4.366e+02
## D.nwrds.unq.log -9.776e+01 4.614e+02
## D.terms.n.post.stem.log -9.776e+01 4.614e+02
## D.nchrs.log -3.061e+02 1.541e+02
## D.nuppr.log 2.734e+02 1.356e+02
## D.npnct24.log -1.147e+02 1.860e+02
## D.TfIdf.sum.post.stem -1.458e+01 3.831e+02
## D.sum.TfIdf -1.458e+01 3.831e+02
## D.TfIdf.sum.post.stop 2.794e+01 6.327e+01
## D.ratio.sum.TfIdf.nwrds -1.024e+01 1.656e+01
## biddable -1.393e+02 7.444e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` -2.137e+01 3.468e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.632e+01 4.090e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.706e+01 5.509e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -7.609e+01 5.699e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.953e+01 3.171e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` -2.224e+00 2.392e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -1.340e+00 4.098e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.000e+00 6.601e+02
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.000e+00 6.601e+02
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.000e+00 6.601e+02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.400e+01 4.138e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.217e+02 4.695e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.000e+00 6.601e+02
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.000e+00 6.601e+02
## t value Pr(>|t|)
## (Intercept) 0.512 0.609092
## `prdline.my.fctriPad 1` -4.141 3.83e-05 ***
## `prdline.my.fctriPad 2` -2.076 0.038247 *
## `prdline.my.fctriPad 3+` -0.066 0.947749
## prdline.my.fctriPadAir 5.067 5.05e-07 ***
## prdline.my.fctriPadmini -1.162 0.245602
## `prdline.my.fctriPadmini 2+` 2.070 0.038784 *
## `condition.fctrFor parts or not working` -4.208 2.88e-05 ***
## `condition.fctrManufacturer refurbished` -0.500 0.617487
## condition.fctrNew 5.748 1.29e-08 ***
## `condition.fctrNew other (see details)` 3.246 0.001220 **
## `condition.fctrSeller refurbished` -1.913 0.056093 .
## D.ratio.nstopwrds.nwrds 0.013 0.989886
## D.TfIdf.sum.stem.stop.Ratio 0.720 0.471944
## color.fctrGold -0.106 0.915773
## `color.fctrSpace Gray` 1.251 0.211227
## color.fctrUnknown -0.586 0.558186
## color.fctrWhite 2.127 0.033753 *
## carrier.fctrNone 0.049 0.960650
## carrier.fctrOther 2.017 0.044071 *
## carrier.fctrSprint -1.319 0.187405
## `carrier.fctrT-Mobile` 0.392 0.695096
## carrier.fctrUnknown 0.773 0.439762
## carrier.fctrVerizon 0.627 0.530518
## storage.fctr16 -6.865 1.35e-11 ***
## storage.fctr32 -5.922 4.74e-09 ***
## storage.fctr64 -4.298 1.94e-05 ***
## storage.fctrUnknown -3.429 0.000638 ***
## D.npnct14.log -0.111 0.911648
## cellular.fctr1 0.045 0.963821
## cellular.fctrUnknown -0.095 0.924509
## D.terms.n.stem.stop.Ratio -0.633 0.527150
## D.ndgts.log 0.033 0.974042
## .rnorm -0.281 0.778623
## idseq.my -1.888 0.059454 .
## D.npnct08.log 0.499 0.618127
## D.npnct05.log -0.501 0.616263
## D.npnct15.log -0.607 0.544179
## D.npnct01.log 1.375 0.169557
## D.npnct16.log 0.654 0.513462
## D.npnct12.log 0.417 0.676476
## D.npnct06.log 0.468 0.640041
## D.npnct03.log -0.550 0.582329
## D.nstopwrds.log -1.023 0.306681
## D.npnct11.log -1.957 0.050753 .
## D.npnct13.log -1.430 0.153199
## D.terms.n.post.stop -0.903 0.367046
## D.terms.n.post.stem 0.890 0.373898
## D.nwrds.log 2.436 0.015059 *
## D.terms.n.post.stop.log 0.319 0.749991
## D.nwrds.unq.log -0.212 0.832251
## D.terms.n.post.stem.log -0.212 0.832251
## D.nchrs.log -1.986 0.047390 *
## D.nuppr.log 2.016 0.044096 *
## D.npnct24.log -0.617 0.537471
## D.TfIdf.sum.post.stem -0.038 0.969657
## D.sum.TfIdf -0.038 0.969657
## D.TfIdf.sum.post.stop 0.442 0.658911
## D.ratio.sum.TfIdf.nwrds -0.618 0.536694
## biddable -18.715 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` -0.616 0.537930
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.399 0.689949
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.310 0.756851
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.335 0.182197
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.616 0.538010
## `prdline.my.fctriPadmini:.clusterid.fctr2` -0.093 0.925947
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.033 0.973929
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.000 1.000000
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.305 0.192309
## `prdline.my.fctriPadAir:.clusterid.fctr3` -2.592 0.009714 **
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.000 1.000000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7409.832)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 5824128 on 786 degrees of freedom
## AIC: 10176
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.428 0.344
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6056393 93.87814 0.5971059 135.1617 10176.28
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4991002 2.970354 0.02413754
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_bayesglm 4 0 140.085 143.666 3.581
## 5 fit.models_1_glmnet 5 0 143.667 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 12.6 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 7300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 73 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.678588e+02
## prdline.my.fctriPad 1
## -5.672999e+01
## prdline.my.fctriPad 2
## -2.419078e+01
## prdline.my.fctriPad 3+
## 1.751996e+00
## prdline.my.fctriPadAir
## 8.652267e+01
## prdline.my.fctriPadmini
## -1.066985e+01
## prdline.my.fctriPadmini 2+
## 4.116040e+01
## condition.fctrFor parts or not working
## -4.784534e+01
## condition.fctrNew
## 7.011983e+01
## condition.fctrNew other (see details)
## 4.570094e+01
## condition.fctrSeller refurbished
## -1.802715e+01
## D.TfIdf.sum.stem.stop.Ratio
## 9.106166e+01
## color.fctrGold
## 5.795334e+00
## color.fctrSpace Gray
## 1.373018e+01
## color.fctrUnknown
## -3.340703e+00
## color.fctrWhite
## 1.664963e+01
## carrier.fctrOther
## 6.270474e+01
## carrier.fctrSprint
## -2.079290e+01
## carrier.fctrT-Mobile
## 5.716868e+00
## carrier.fctrVerizon
## 3.488365e+00
## storage.fctr16
## -4.478141e+01
## storage.fctr32
## -2.818201e+01
## storage.fctr64
## 6.306139e-01
## cellular.fctr1
## 4.098586e+00
## cellular.fctrUnknown
## -2.664368e+01
## D.terms.n.stem.stop.Ratio
## -8.876287e+00
## idseq.my
## -7.129887e-03
## D.npnct08.log
## 1.840585e+00
## D.npnct05.log
## -3.788271e+01
## D.npnct15.log
## -1.058358e+01
## D.npnct01.log
## 3.246750e+00
## D.npnct16.log
## 2.233837e+01
## D.nstopwrds.log
## 2.246519e+00
## D.npnct11.log
## -8.478520e+00
## D.npnct13.log
## -5.650180e+00
## D.terms.n.post.stop
## 2.467025e-01
## D.terms.n.post.stem
## 1.485964e-01
## D.ratio.sum.TfIdf.nwrds
## -1.790748e+01
## biddable
## -1.222363e+02
## prdline.my.fctriPad 2:.clusterid.fctr2
## 2.653083e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.764589e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 5.569699e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -3.790117e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -5.938492e+01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 280.65528100
## prdline.my.fctriPad 1
## -68.79441626
## prdline.my.fctriPad 2
## -33.19811811
## prdline.my.fctriPad 3+
## 0.36406869
## prdline.my.fctriPadAir
## 86.21498610
## prdline.my.fctriPadmini
## -18.36867378
## prdline.my.fctriPadmini 2+
## 37.42235585
## condition.fctrFor parts or not working
## -51.07404077
## condition.fctrManufacturer refurbished
## -12.24214250
## condition.fctrNew
## 67.70631623
## condition.fctrNew other (see details)
## 51.88101929
## condition.fctrSeller refurbished
## -27.67583177
## D.ratio.nstopwrds.nwrds
## -6.13458548
## D.TfIdf.sum.stem.stop.Ratio
## 135.39456042
## color.fctrGold
## -1.43461369
## color.fctrSpace Gray
## 15.70598147
## color.fctrUnknown
## -5.23923552
## color.fctrWhite
## 19.60274033
## carrier.fctrNone
## 2.02587391
## carrier.fctrOther
## 119.96114616
## carrier.fctrSprint
## -32.32233483
## carrier.fctrT-Mobile
## 11.46088122
## carrier.fctrUnknown
## 12.44770174
## carrier.fctrVerizon
## 9.67393658
## storage.fctr16
## -134.27534383
## storage.fctr32
## -120.08129410
## storage.fctr64
## -85.51493972
## storage.fctrUnknown
## -90.55450711
## D.npnct14.log
## -13.77399627
## cellular.fctrUnknown
## -41.02744672
## D.terms.n.stem.stop.Ratio
## -50.75752981
## D.ndgts.log
## -3.20013511
## .rnorm
## -1.02200518
## idseq.my
## -0.01345120
## D.npnct08.log
## 5.71799771
## D.npnct05.log
## -59.52158282
## D.npnct15.log
## -30.85088047
## D.npnct01.log
## 12.58709547
## D.npnct16.log
## 37.48625338
## D.npnct12.log
## 9.33139195
## D.npnct06.log
## 19.52446800
## D.npnct03.log
## -33.29681198
## D.nstopwrds.log
## -30.01250682
## D.npnct11.log
## -29.37784533
## D.npnct13.log
## -27.08295920
## D.terms.n.post.stop
## -3.04185634
## D.terms.n.post.stem
## 3.02961656
## D.nwrds.log
## 83.63630823
## D.terms.n.post.stop.log
## -5.40866938
## D.nwrds.unq.log
## -0.48008525
## D.terms.n.post.stem.log
## -0.04428734
## D.nchrs.log
## -8.14068251
## D.nuppr.log
## 11.59217298
## D.npnct24.log
## -196.89141495
## D.TfIdf.sum.post.stem
## -1.24786709
## D.sum.TfIdf
## -0.24787163
## D.TfIdf.sum.post.stop
## 0.78088389
## D.ratio.sum.TfIdf.nwrds
## -6.56526941
## biddable
## -138.53425691
## prdline.my.fctrUnknown:.clusterid.fctr2
## -18.45839872
## prdline.my.fctriPad 1:.clusterid.fctr2
## 16.57600871
## prdline.my.fctriPad 2:.clusterid.fctr2
## 22.71522579
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -67.17286504
## prdline.my.fctriPadAir:.clusterid.fctr2
## -17.35200704
## prdline.my.fctriPadmini:.clusterid.fctr2
## -2.84683699
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## -0.46861121
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -47.92488774
## prdline.my.fctriPadAir:.clusterid.fctr3
## -115.57742115
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.676 0.054
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.5761356 89.92115 0.5750594 138.8104 0.5328404
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.551696 0.04348867
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_glmnet 5 0 143.667 147.203 3.536
## 6 fit.models_1_rpart 6 0 147.203 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0803 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.0000000
## 2 0.08271687 1 0.7705890
## 3 0.08034499 2 0.6878721
##
## Variable importance
## biddable
## 66
## prdline.my.fctriPadAir
## 24
## idseq.my
## 3
## color.fctrGold
## 3
## D.npnct15.log
## 1
## prdline.my.fctriPad 2:.clusterid.fctr2
## 1
## condition.fctrManufacturer refurbished
## 1
## D.TfIdf.sum.stem.stop.Ratio
## 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.22941100, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.14781390, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.13039270, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.05958729, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.05938979, (0 missing)
## Surrogate splits:
## idseq.my < 1783.5 to the left, agree=0.757, adj=0.050, (0 split)
## D.npnct15.log < 0.3465736 to the left, agree=0.750, adj=0.023, (0 split)
## prdline.my.fctriPad 2:.clusterid.fctr2 < 0.5 to the left, agree=0.748, adj=0.014, (0 split)
## D.TfIdf.sum.stem.stop.Ratio < 0.8214259 to the right, agree=0.747, adj=0.009, (0 split)
## D.npnct01.log < 1.242453 to the left, agree=0.745, adj=0.005, (0 split)
##
## Node number 2: 640 observations
## mean=90.63711, MSE=11139.65
##
## Node number 3: 220 observations, complexity param=0.08271687
## mean=234.4917, MSE=19323.14
## left son=6 (183 obs) right son=7 (37 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.28736310, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.18444820, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.17624070, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.13962800, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.09007232, (0 missing)
## Surrogate splits:
## color.fctrGold < 0.5 to the left, agree=0.855, adj=0.135, (0 split)
## condition.fctrManufacturer refurbished < 0.5 to the left, agree=0.836, adj=0.027, (0 split)
##
## Node number 6: 183 observations
## mean=200.9851, MSE=13424.58
##
## Node number 7: 37 observations
## mean=400.2132, MSE=15480.72
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530.0 127.43710
## 2) biddable>=0.5 640 7129375.0 90.63711 *
## 3) biddable< 0.5 220 4251091.0 234.49170
## 6) prdline.my.fctriPadAir< 0.5 183 2456698.0 200.98510 *
## 7) prdline.my.fctriPadAir>=0.5 37 572786.8 400.21320 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.377 0.053
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.3121279 111.8385 0.450545 157.8425 0.2750573
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.592112 0.04148092
## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rpart 6 0 147.203 150.754 3.552
## 7 fit.models_1_rf 7 0 150.755 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 37 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 72 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 72 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 22.449 7.239
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.8913483 91.20519 0.6232976 130.5761 0.5247944
## min.RMSESD.fit max.RsquaredSD.fit
## 1 5.59635 0.05641606
## label step_major step_minor bgn end elapsed
## 7 fit.models_1_rf 7 0 150.755 175.673 24.918
## 8 fit.models_1_lm 8 0 175.673 NA NA
## [1] "fitting model: All.Interact.X.lm"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 64, 361, 391, 403, 435, 442, 462, 495, 532, 642, 665, 817, 824
## Warning: not plotting observations with leverage one:
## 64, 361, 391, 403, 435, 442, 462, 495, 532, 642, 665, 817, 824
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -298.79 -33.39 0.00 33.16 363.23
##
## Coefficients: (32 not defined because of singularities)
## Estimate
## (Intercept) -3.282e+04
## `prdline.my.fctriPad 1` 1.098e+03
## `prdline.my.fctriPad 2` 1.205e+03
## `prdline.my.fctriPad 3+` 1.058e+03
## prdline.my.fctriPadAir 9.484e+02
## prdline.my.fctriPadmini 1.594e+03
## `prdline.my.fctriPadmini 2+` 1.139e+03
## D.ratio.nstopwrds.nwrds -1.551e+02
## D.npnct14.log -2.551e+01
## D.terms.n.stem.stop.Ratio 3.171e+04
## D.ndgts.log 2.513e+01
## .rnorm 1.835e+00
## D.npnct05.log -9.931e+01
## D.npnct15.log 6.400e+00
## D.npnct12.log -5.514e+00
## D.npnct06.log -7.626e+01
## D.npnct03.log -6.539e+00
## D.npnct11.log -1.533e+01
## D.npnct13.log 1.976e+00
## D.nwrds.log 1.954e+02
## D.terms.n.post.stop.log 3.504e+04
## D.nwrds.unq.log -3.514e+04
## D.terms.n.post.stem.log NA
## D.nuppr.log 2.501e+02
## D.npnct24.log 3.967e+01
## D.TfIdf.sum.post.stem -4.977e+00
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 1.080e+01
## D.ratio.sum.TfIdf.nwrds -2.458e+01
## D.nchrs.log -3.177e+02
## D.TfIdf.sum.stem.stop.Ratio 1.361e+03
## D.npnct16.log 1.874e+02
## D.npnct01.log 2.994e+02
## D.nstopwrds.log -5.466e+01
## D.npnct08.log 6.453e+01
## D.terms.n.post.stop -6.808e+01
## D.terms.n.post.stem 8.859e+01
## biddable -1.178e+02
## `condition.fctrFor parts or not working` -6.056e+01
## `condition.fctrManufacturer refurbished` 7.858e+01
## condition.fctrNew 6.644e+01
## `condition.fctrNew other (see details)` 2.795e+01
## `condition.fctrSeller refurbished` -4.074e+01
## color.fctrGold -1.458e+01
## `color.fctrSpace Gray` 9.217e+01
## color.fctrUnknown 3.472e+01
## color.fctrWhite 8.049e+01
## storage.fctr16 5.563e+01
## storage.fctr32 -6.953e+01
## storage.fctr64 1.190e+02
## storage.fctrUnknown 4.559e+01
## idseq.my 1.099e-02
## cellular.fctr1 7.625e+00
## cellular.fctrUnknown -4.651e+01
## carrier.fctrNone NA
## carrier.fctrOther 6.356e+01
## carrier.fctrSprint -5.663e+01
## `carrier.fctrT-Mobile` 2.297e+01
## carrier.fctrUnknown 2.121e+01
## carrier.fctrVerizon 8.838e+00
## `prdline.my.fctriPad 1:D.nchrs.log` -9.472e+00
## `prdline.my.fctriPad 2:D.nchrs.log` -5.821e+00
## `prdline.my.fctriPad 3+:D.nchrs.log` 1.375e+00
## `prdline.my.fctriPadAir:D.nchrs.log` -4.913e+01
## `prdline.my.fctriPadmini:D.nchrs.log` -2.375e+01
## `prdline.my.fctriPadmini 2+:D.nchrs.log` -2.027e+00
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -1.103e+03
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -1.197e+03
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -9.407e+02
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` -5.274e+02
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -1.548e+03
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` -7.679e+02
## `prdline.my.fctriPad 1:D.npnct16.log` -8.155e+01
## `prdline.my.fctriPad 2:D.npnct16.log` -1.076e+02
## `prdline.my.fctriPad 3+:D.npnct16.log` -2.163e+02
## `prdline.my.fctriPadAir:D.npnct16.log` -6.338e+01
## `prdline.my.fctriPadmini:D.npnct16.log` -1.329e+02
## `prdline.my.fctriPadmini 2+:D.npnct16.log` -1.191e+02
## `prdline.my.fctriPad 1:D.npnct01.log` -3.019e+02
## `prdline.my.fctriPad 2:D.npnct01.log` -2.867e+02
## `prdline.my.fctriPad 3+:D.npnct01.log` -3.639e+02
## `prdline.my.fctriPadAir:D.npnct01.log` -1.117e+02
## `prdline.my.fctriPadmini:D.npnct01.log` -2.869e+02
## `prdline.my.fctriPadmini 2+:D.npnct01.log` -2.313e+02
## `prdline.my.fctriPad 1:D.nstopwrds.log` 1.116e+01
## `prdline.my.fctriPad 2:D.nstopwrds.log` 3.339e+01
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 3.721e+01
## `prdline.my.fctriPadAir:D.nstopwrds.log` 6.400e+01
## `prdline.my.fctriPadmini:D.nstopwrds.log` 1.997e+01
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 2.432e+01
## `prdline.my.fctriPad 1:D.npnct08.log` -8.081e+01
## `prdline.my.fctriPad 2:D.npnct08.log` -8.064e+01
## `prdline.my.fctriPad 3+:D.npnct08.log` -2.832e+01
## `prdline.my.fctriPadAir:D.npnct08.log` 4.020e+01
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` -6.096e+00
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -1.598e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.230e+02
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.195e+02
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -8.223e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.704e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 2.120e+01
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 1.446e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 1.009e+02
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 9.359e+01
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 7.301e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.572e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` -4.481e+01
## `prdline.my.fctriPad 1:biddable` 6.522e+01
## `prdline.my.fctriPad 2:biddable` 2.063e+01
## `prdline.my.fctriPad 3+:biddable` -2.495e+01
## `prdline.my.fctriPadAir:biddable` -9.896e+01
## `prdline.my.fctriPadmini:biddable` 2.004e+01
## `prdline.my.fctriPadmini 2+:biddable` -6.809e+01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 4.722e+00
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 4.368e+01
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -3.736e+00
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -3.367e+00
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 1.887e+00
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 2.712e+01
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -1.408e+02
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` -1.005e+02
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -1.245e+02
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -1.508e+02
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 3.882e+00
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -2.265e+02
## `prdline.my.fctriPad 1:condition.fctrNew` 2.799e+01
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -4.425e+01
## `prdline.my.fctriPadAir:condition.fctrNew` -5.265e+00
## `prdline.my.fctriPadmini:condition.fctrNew` -1.595e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 7.394e+00
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -8.472e+01
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -3.739e+01
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -7.181e-01
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 3.778e+01
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 1.895e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.282e+02
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 2.197e+01
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 2.749e+01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 1.075e+01
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -7.248e+01
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 7.142e+01
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` -1.867e+01
## `prdline.my.fctriPadAir:color.fctrGold` 5.798e+01
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` -1.398e+01
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 1.637e+01
## `prdline.my.fctriPadAir:color.fctrSpace Gray` -3.258e+01
## `prdline.my.fctriPadmini:color.fctrSpace Gray` -6.470e+01
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` -7.693e+01
## `prdline.my.fctriPad 1:color.fctrUnknown` -2.776e+01
## `prdline.my.fctriPad 2:color.fctrUnknown` -7.152e+01
## `prdline.my.fctriPad 3+:color.fctrUnknown` -6.751e+01
## `prdline.my.fctriPadAir:color.fctrUnknown` 2.352e+01
## `prdline.my.fctriPadmini:color.fctrUnknown` -8.298e+00
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` -4.604e+01
## `prdline.my.fctriPad 1:color.fctrWhite` -8.990e+01
## `prdline.my.fctriPad 2:color.fctrWhite` -8.756e+01
## `prdline.my.fctriPad 3+:color.fctrWhite` -6.675e+01
## `prdline.my.fctriPadAir:color.fctrWhite` -1.519e+01
## `prdline.my.fctriPadmini:color.fctrWhite` -2.824e+01
## `prdline.my.fctriPadmini 2+:color.fctrWhite` -4.720e+01
## `prdline.my.fctriPad 1:storage.fctr16` -6.196e+01
## `prdline.my.fctriPad 2:storage.fctr16` 9.371e+00
## `prdline.my.fctriPad 3+:storage.fctr16` -3.010e+01
## `prdline.my.fctriPadAir:storage.fctr16` -2.312e+02
## `prdline.my.fctriPadmini:storage.fctr16` -5.003e+01
## `prdline.my.fctriPadmini 2+:storage.fctr16` -2.102e+02
## `prdline.my.fctriPad 1:storage.fctr32` 6.326e+01
## `prdline.my.fctriPad 2:storage.fctr32` 1.440e+02
## `prdline.my.fctriPad 3+:storage.fctr32` 1.074e+02
## `prdline.my.fctriPadAir:storage.fctr32` -9.784e+01
## `prdline.my.fctriPadmini:storage.fctr32` 8.647e+01
## `prdline.my.fctriPadmini 2+:storage.fctr32` -2.104e+01
## `prdline.my.fctriPad 1:storage.fctr64` -1.106e+02
## `prdline.my.fctriPad 2:storage.fctr64` -3.832e+01
## `prdline.my.fctriPad 3+:storage.fctr64` -5.786e+01
## `prdline.my.fctriPadAir:storage.fctr64` -2.054e+02
## `prdline.my.fctriPadmini:storage.fctr64` -7.135e+01
## `prdline.my.fctriPadmini 2+:storage.fctr64` -1.879e+02
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 4.638e+01
## `prdline.my.fctriPadAir:storage.fctrUnknown` -5.528e+02
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` -1.079e-02
## `prdline.my.fctriPad 2:idseq.my` -1.496e-02
## `prdline.my.fctriPad 3+:idseq.my` -2.315e-02
## `prdline.my.fctriPadAir:idseq.my` -5.091e-02
## `prdline.my.fctriPadmini:idseq.my` -1.292e-02
## `prdline.my.fctriPadmini 2+:idseq.my` -8.014e-02
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` -1.247e+02
## `prdline.my.fctriPad 1:.clusterid.fctr2` -6.969e+00
## `prdline.my.fctriPad 2:.clusterid.fctr2` 5.321e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -4.223e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.391e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 6.355e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.186e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -4.225e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -7.546e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
## Std. Error
## (Intercept) 1.038e+05
## `prdline.my.fctriPad 1` 5.752e+02
## `prdline.my.fctriPad 2` 5.570e+02
## `prdline.my.fctriPad 3+` 5.650e+02
## prdline.my.fctriPadAir 6.689e+02
## prdline.my.fctriPadmini 5.773e+02
## `prdline.my.fctriPadmini 2+` 8.371e+02
## D.ratio.nstopwrds.nwrds 2.851e+02
## D.npnct14.log 3.985e+01
## D.terms.n.stem.stop.Ratio 1.038e+05
## D.ndgts.log 2.301e+01
## .rnorm 3.097e+00
## D.npnct05.log 8.842e+01
## D.npnct15.log 3.735e+01
## D.npnct12.log 2.707e+01
## D.npnct06.log 9.464e+01
## D.npnct03.log 6.897e+01
## D.npnct11.log 1.564e+01
## D.npnct13.log 1.654e+01
## D.nwrds.log 1.048e+02
## D.terms.n.post.stop.log 1.164e+05
## D.nwrds.unq.log 1.164e+05
## D.terms.n.post.stem.log NA
## D.nuppr.log 2.067e+02
## D.npnct24.log 2.368e+02
## D.TfIdf.sum.post.stem 1.056e+02
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 1.006e+02
## D.ratio.sum.TfIdf.nwrds 1.983e+01
## D.nchrs.log 2.390e+02
## D.TfIdf.sum.stem.stop.Ratio 8.323e+02
## D.npnct16.log 9.772e+01
## D.npnct01.log 1.363e+02
## D.nstopwrds.log 8.926e+01
## D.npnct08.log 9.575e+01
## D.terms.n.post.stop 7.377e+02
## D.terms.n.post.stem 7.394e+02
## biddable 2.680e+01
## `condition.fctrFor parts or not working` 3.678e+01
## `condition.fctrManufacturer refurbished` 9.016e+01
## condition.fctrNew 3.435e+01
## `condition.fctrNew other (see details)` 6.242e+01
## `condition.fctrSeller refurbished` 5.312e+01
## color.fctrGold 6.009e+01
## `color.fctrSpace Gray` 4.735e+01
## color.fctrUnknown 3.291e+01
## color.fctrWhite 3.964e+01
## storage.fctr16 8.338e+01
## storage.fctr32 9.843e+01
## storage.fctr64 9.833e+01
## storage.fctrUnknown 7.991e+01
## idseq.my 3.213e-02
## cellular.fctr1 1.078e+01
## cellular.fctrUnknown 2.558e+01
## carrier.fctrNone NA
## carrier.fctrOther 7.454e+01
## carrier.fctrSprint 2.851e+01
## `carrier.fctrT-Mobile` 3.345e+01
## carrier.fctrUnknown 1.739e+01
## carrier.fctrVerizon 1.488e+01
## `prdline.my.fctriPad 1:D.nchrs.log` 2.659e+01
## `prdline.my.fctriPad 2:D.nchrs.log` 2.388e+01
## `prdline.my.fctriPad 3+:D.nchrs.log` 2.428e+01
## `prdline.my.fctriPadAir:D.nchrs.log` 2.469e+01
## `prdline.my.fctriPadmini:D.nchrs.log` 2.537e+01
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 3.518e+01
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 5.599e+02
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 5.438e+02
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 5.421e+02
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 6.485e+02
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 5.667e+02
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 8.553e+02
## `prdline.my.fctriPad 1:D.npnct16.log` 9.599e+01
## `prdline.my.fctriPad 2:D.npnct16.log` 1.246e+02
## `prdline.my.fctriPad 3+:D.npnct16.log` 1.320e+02
## `prdline.my.fctriPadAir:D.npnct16.log` 1.187e+02
## `prdline.my.fctriPadmini:D.npnct16.log` 9.100e+01
## `prdline.my.fctriPadmini 2+:D.npnct16.log` 1.482e+02
## `prdline.my.fctriPad 1:D.npnct01.log` 1.461e+02
## `prdline.my.fctriPad 2:D.npnct01.log` 1.590e+02
## `prdline.my.fctriPad 3+:D.npnct01.log` 1.505e+02
## `prdline.my.fctriPadAir:D.npnct01.log` 1.485e+02
## `prdline.my.fctriPadmini:D.npnct01.log` 1.410e+02
## `prdline.my.fctriPadmini 2+:D.npnct01.log` 1.645e+02
## `prdline.my.fctriPad 1:D.nstopwrds.log` 4.126e+01
## `prdline.my.fctriPad 2:D.nstopwrds.log` 4.059e+01
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 3.889e+01
## `prdline.my.fctriPadAir:D.nstopwrds.log` 3.971e+01
## `prdline.my.fctriPadmini:D.nstopwrds.log` 4.146e+01
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 6.424e+01
## `prdline.my.fctriPad 1:D.npnct08.log` 1.287e+02
## `prdline.my.fctriPad 2:D.npnct08.log` 1.036e+02
## `prdline.my.fctriPad 3+:D.npnct08.log` 1.081e+02
## `prdline.my.fctriPadAir:D.npnct08.log` 1.185e+02
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 1.500e+02
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.176e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 8.021e+01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 7.728e+01
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 8.386e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 1.056e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 1.113e+02
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 1.164e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 7.736e+01
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 7.418e+01
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 8.158e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.052e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` 1.079e+02
## `prdline.my.fctriPad 1:biddable` 3.433e+01
## `prdline.my.fctriPad 2:biddable` 3.295e+01
## `prdline.my.fctriPad 3+:biddable` 3.365e+01
## `prdline.my.fctriPadAir:biddable` 3.229e+01
## `prdline.my.fctriPadmini:biddable` 3.287e+01
## `prdline.my.fctriPadmini 2+:biddable` 3.651e+01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 5.117e+01
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 4.943e+01
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 4.858e+01
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 5.072e+01
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 4.568e+01
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 8.457e+01
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 1.215e+02
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 1.031e+02
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 1.080e+02
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.009e+02
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 1.155e+02
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 1.261e+02
## `prdline.my.fctriPad 1:condition.fctrNew` 9.177e+01
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 9.102e+01
## `prdline.my.fctriPadAir:condition.fctrNew` 3.918e+01
## `prdline.my.fctriPadmini:condition.fctrNew` 4.459e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 4.312e+01
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 1.088e+02
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 7.915e+01
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 7.738e+01
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 6.762e+01
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 7.767e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.016e+02
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 6.313e+01
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 6.300e+01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 6.424e+01
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 8.035e+01
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 7.268e+01
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` 1.366e+02
## `prdline.my.fctriPadAir:color.fctrGold` 7.525e+01
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 7.366e+01
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 7.228e+01
## `prdline.my.fctriPadAir:color.fctrSpace Gray` 6.221e+01
## `prdline.my.fctriPadmini:color.fctrSpace Gray` 5.463e+01
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` 6.413e+01
## `prdline.my.fctriPad 1:color.fctrUnknown` 3.760e+01
## `prdline.my.fctriPad 2:color.fctrUnknown` 3.774e+01
## `prdline.my.fctriPad 3+:color.fctrUnknown` 3.772e+01
## `prdline.my.fctriPadAir:color.fctrUnknown` 5.169e+01
## `prdline.my.fctriPadmini:color.fctrUnknown` 3.869e+01
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` 5.529e+01
## `prdline.my.fctriPad 1:color.fctrWhite` 5.186e+01
## `prdline.my.fctriPad 2:color.fctrWhite` 4.417e+01
## `prdline.my.fctriPad 3+:color.fctrWhite` 4.394e+01
## `prdline.my.fctriPadAir:color.fctrWhite` 5.728e+01
## `prdline.my.fctriPadmini:color.fctrWhite` 4.777e+01
## `prdline.my.fctriPadmini 2+:color.fctrWhite` 5.797e+01
## `prdline.my.fctriPad 1:storage.fctr16` 5.335e+01
## `prdline.my.fctriPad 2:storage.fctr16` 6.579e+01
## `prdline.my.fctriPad 3+:storage.fctr16` 1.036e+02
## `prdline.my.fctriPadAir:storage.fctr16` 8.712e+01
## `prdline.my.fctriPadmini:storage.fctr16` 4.462e+01
## `prdline.my.fctriPadmini 2+:storage.fctr16` 7.790e+01
## `prdline.my.fctriPad 1:storage.fctr32` 7.227e+01
## `prdline.my.fctriPad 2:storage.fctr32` 8.384e+01
## `prdline.my.fctriPad 3+:storage.fctr32` 1.165e+02
## `prdline.my.fctriPadAir:storage.fctr32` 1.023e+02
## `prdline.my.fctriPadmini:storage.fctr32` 7.298e+01
## `prdline.my.fctriPadmini 2+:storage.fctr32` 9.657e+01
## `prdline.my.fctriPad 1:storage.fctr64` 7.468e+01
## `prdline.my.fctriPad 2:storage.fctr64` 8.734e+01
## `prdline.my.fctriPad 3+:storage.fctr64` 1.170e+02
## `prdline.my.fctriPadAir:storage.fctr64` 1.017e+02
## `prdline.my.fctriPadmini:storage.fctr64` 7.253e+01
## `prdline.my.fctriPadmini 2+:storage.fctr64` 9.592e+01
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 1.097e+02
## `prdline.my.fctriPadAir:storage.fctrUnknown` 1.635e+02
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` 3.835e-02
## `prdline.my.fctriPad 2:idseq.my` 3.861e-02
## `prdline.my.fctriPad 3+:idseq.my` 3.702e-02
## `prdline.my.fctriPadAir:idseq.my` 3.644e-02
## `prdline.my.fctriPadmini:idseq.my` 3.698e-02
## `prdline.my.fctriPadmini 2+:idseq.my` 4.023e-02
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 4.878e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 4.513e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 6.483e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 6.057e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 3.594e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.218e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 5.073e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 4.619e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 6.465e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
## t value
## (Intercept) -0.316
## `prdline.my.fctriPad 1` 1.909
## `prdline.my.fctriPad 2` 2.164
## `prdline.my.fctriPad 3+` 1.873
## prdline.my.fctriPadAir 1.418
## prdline.my.fctriPadmini 2.761
## `prdline.my.fctriPadmini 2+` 1.360
## D.ratio.nstopwrds.nwrds -0.544
## D.npnct14.log -0.640
## D.terms.n.stem.stop.Ratio 0.305
## D.ndgts.log 1.092
## .rnorm 0.592
## D.npnct05.log -1.123
## D.npnct15.log 0.171
## D.npnct12.log -0.204
## D.npnct06.log -0.806
## D.npnct03.log -0.095
## D.npnct11.log -0.980
## D.npnct13.log 0.119
## D.nwrds.log 1.864
## D.terms.n.post.stop.log 0.301
## D.nwrds.unq.log -0.302
## D.terms.n.post.stem.log NA
## D.nuppr.log 1.210
## D.npnct24.log 0.168
## D.TfIdf.sum.post.stem -0.047
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 0.107
## D.ratio.sum.TfIdf.nwrds -1.240
## D.nchrs.log -1.329
## D.TfIdf.sum.stem.stop.Ratio 1.635
## D.npnct16.log 1.918
## D.npnct01.log 2.197
## D.nstopwrds.log -0.612
## D.npnct08.log 0.674
## D.terms.n.post.stop -0.092
## D.terms.n.post.stem 0.120
## biddable -4.396
## `condition.fctrFor parts or not working` -1.647
## `condition.fctrManufacturer refurbished` 0.872
## condition.fctrNew 1.934
## `condition.fctrNew other (see details)` 0.448
## `condition.fctrSeller refurbished` -0.767
## color.fctrGold -0.243
## `color.fctrSpace Gray` 1.947
## color.fctrUnknown 1.055
## color.fctrWhite 2.031
## storage.fctr16 0.667
## storage.fctr32 -0.706
## storage.fctr64 1.210
## storage.fctrUnknown 0.571
## idseq.my 0.342
## cellular.fctr1 0.707
## cellular.fctrUnknown -1.818
## carrier.fctrNone NA
## carrier.fctrOther 0.853
## carrier.fctrSprint -1.986
## `carrier.fctrT-Mobile` 0.687
## carrier.fctrUnknown 1.220
## carrier.fctrVerizon 0.594
## `prdline.my.fctriPad 1:D.nchrs.log` -0.356
## `prdline.my.fctriPad 2:D.nchrs.log` -0.244
## `prdline.my.fctriPad 3+:D.nchrs.log` 0.057
## `prdline.my.fctriPadAir:D.nchrs.log` -1.990
## `prdline.my.fctriPadmini:D.nchrs.log` -0.936
## `prdline.my.fctriPadmini 2+:D.nchrs.log` -0.058
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -1.970
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -2.201
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -1.735
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` -0.813
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -2.731
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` -0.898
## `prdline.my.fctriPad 1:D.npnct16.log` -0.850
## `prdline.my.fctriPad 2:D.npnct16.log` -0.864
## `prdline.my.fctriPad 3+:D.npnct16.log` -1.639
## `prdline.my.fctriPadAir:D.npnct16.log` -0.534
## `prdline.my.fctriPadmini:D.npnct16.log` -1.461
## `prdline.my.fctriPadmini 2+:D.npnct16.log` -0.804
## `prdline.my.fctriPad 1:D.npnct01.log` -2.066
## `prdline.my.fctriPad 2:D.npnct01.log` -1.803
## `prdline.my.fctriPad 3+:D.npnct01.log` -2.418
## `prdline.my.fctriPadAir:D.npnct01.log` -0.752
## `prdline.my.fctriPadmini:D.npnct01.log` -2.035
## `prdline.my.fctriPadmini 2+:D.npnct01.log` -1.406
## `prdline.my.fctriPad 1:D.nstopwrds.log` 0.271
## `prdline.my.fctriPad 2:D.nstopwrds.log` 0.823
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 0.957
## `prdline.my.fctriPadAir:D.nstopwrds.log` 1.612
## `prdline.my.fctriPadmini:D.nstopwrds.log` 0.482
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 0.379
## `prdline.my.fctriPad 1:D.npnct08.log` -0.628
## `prdline.my.fctriPad 2:D.npnct08.log` -0.778
## `prdline.my.fctriPad 3+:D.npnct08.log` -0.262
## `prdline.my.fctriPadAir:D.npnct08.log` 0.339
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` -0.041
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -1.359
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.534
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.546
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.981
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.613
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.190
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 1.243
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 1.304
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 1.262
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 0.895
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.495
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` -0.415
## `prdline.my.fctriPad 1:biddable` 1.900
## `prdline.my.fctriPad 2:biddable` 0.626
## `prdline.my.fctriPad 3+:biddable` -0.742
## `prdline.my.fctriPadAir:biddable` -3.065
## `prdline.my.fctriPadmini:biddable` 0.610
## `prdline.my.fctriPadmini 2+:biddable` -1.865
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.092
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.884
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.077
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.066
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.041
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.321
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -1.158
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` -0.975
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -1.153
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -1.494
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.034
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -1.796
## `prdline.my.fctriPad 1:condition.fctrNew` 0.305
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.486
## `prdline.my.fctriPadAir:condition.fctrNew` -0.134
## `prdline.my.fctriPadmini:condition.fctrNew` -0.358
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.172
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.779
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.472
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -0.009
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.559
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.244
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.261
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.348
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.436
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.167
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.902
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.983
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` -0.137
## `prdline.my.fctriPadAir:color.fctrGold` 0.771
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` -0.190
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 0.227
## `prdline.my.fctriPadAir:color.fctrSpace Gray` -0.524
## `prdline.my.fctriPadmini:color.fctrSpace Gray` -1.184
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` -1.200
## `prdline.my.fctriPad 1:color.fctrUnknown` -0.738
## `prdline.my.fctriPad 2:color.fctrUnknown` -1.895
## `prdline.my.fctriPad 3+:color.fctrUnknown` -1.790
## `prdline.my.fctriPadAir:color.fctrUnknown` 0.455
## `prdline.my.fctriPadmini:color.fctrUnknown` -0.214
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` -0.833
## `prdline.my.fctriPad 1:color.fctrWhite` -1.734
## `prdline.my.fctriPad 2:color.fctrWhite` -1.982
## `prdline.my.fctriPad 3+:color.fctrWhite` -1.519
## `prdline.my.fctriPadAir:color.fctrWhite` -0.265
## `prdline.my.fctriPadmini:color.fctrWhite` -0.591
## `prdline.my.fctriPadmini 2+:color.fctrWhite` -0.814
## `prdline.my.fctriPad 1:storage.fctr16` -1.161
## `prdline.my.fctriPad 2:storage.fctr16` 0.142
## `prdline.my.fctriPad 3+:storage.fctr16` -0.290
## `prdline.my.fctriPadAir:storage.fctr16` -2.653
## `prdline.my.fctriPadmini:storage.fctr16` -1.121
## `prdline.my.fctriPadmini 2+:storage.fctr16` -2.698
## `prdline.my.fctriPad 1:storage.fctr32` 0.875
## `prdline.my.fctriPad 2:storage.fctr32` 1.718
## `prdline.my.fctriPad 3+:storage.fctr32` 0.922
## `prdline.my.fctriPadAir:storage.fctr32` -0.956
## `prdline.my.fctriPadmini:storage.fctr32` 1.185
## `prdline.my.fctriPadmini 2+:storage.fctr32` -0.218
## `prdline.my.fctriPad 1:storage.fctr64` -1.481
## `prdline.my.fctriPad 2:storage.fctr64` -0.439
## `prdline.my.fctriPad 3+:storage.fctr64` -0.495
## `prdline.my.fctriPadAir:storage.fctr64` -2.020
## `prdline.my.fctriPadmini:storage.fctr64` -0.984
## `prdline.my.fctriPadmini 2+:storage.fctr64` -1.959
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 0.423
## `prdline.my.fctriPadAir:storage.fctrUnknown` -3.381
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` -0.281
## `prdline.my.fctriPad 2:idseq.my` -0.388
## `prdline.my.fctriPad 3+:idseq.my` -0.625
## `prdline.my.fctriPadAir:idseq.my` -1.397
## `prdline.my.fctriPadmini:idseq.my` -0.349
## `prdline.my.fctriPadmini 2+:idseq.my` -1.992
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` -2.557
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.154
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.821
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.697
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.387
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.197
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.431
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.915
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.167
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
## Pr(>|t|)
## (Intercept) 0.752007
## `prdline.my.fctriPad 1` 0.056750
## `prdline.my.fctriPad 2` 0.030849
## `prdline.my.fctriPad 3+` 0.061573
## prdline.my.fctriPadAir 0.156702
## prdline.my.fctriPadmini 0.005915
## `prdline.my.fctriPadmini 2+` 0.174191
## D.ratio.nstopwrds.nwrds 0.586763
## D.npnct14.log 0.522210
## D.terms.n.stem.stop.Ratio 0.760137
## D.ndgts.log 0.275222
## .rnorm 0.553779
## D.npnct05.log 0.261814
## D.npnct15.log 0.863992
## D.npnct12.log 0.838651
## D.npnct06.log 0.420674
## D.npnct03.log 0.924500
## D.npnct11.log 0.327574
## D.npnct13.log 0.904975
## D.nwrds.log 0.062744
## D.terms.n.post.stop.log 0.763494
## D.nwrds.unq.log 0.762866
## D.terms.n.post.stem.log NA
## D.nuppr.log 0.226744
## D.npnct24.log 0.867016
## D.TfIdf.sum.post.stem 0.962425
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 0.914547
## D.ratio.sum.TfIdf.nwrds 0.215502
## D.nchrs.log 0.184336
## D.TfIdf.sum.stem.stop.Ratio 0.102433
## D.npnct16.log 0.055521
## D.npnct01.log 0.028358
## D.nstopwrds.log 0.540502
## D.npnct08.log 0.500591
## D.terms.n.post.stop 0.926501
## D.terms.n.post.stem 0.904669
## biddable 1.28e-05
## `condition.fctrFor parts or not working` 0.100124
## `condition.fctrManufacturer refurbished` 0.383768
## condition.fctrNew 0.053484
## `condition.fctrNew other (see details)` 0.654457
## `condition.fctrSeller refurbished` 0.443403
## color.fctrGold 0.808388
## `color.fctrSpace Gray` 0.051976
## color.fctrUnknown 0.291854
## color.fctrWhite 0.042671
## storage.fctr16 0.504869
## storage.fctr32 0.480148
## storage.fctr64 0.226551
## storage.fctrUnknown 0.568503
## idseq.my 0.732405
## cellular.fctr1 0.479609
## cellular.fctrUnknown 0.069493
## carrier.fctrNone NA
## carrier.fctrOther 0.394114
## carrier.fctrSprint 0.047431
## `carrier.fctrT-Mobile` 0.492575
## carrier.fctrUnknown 0.222986
## carrier.fctrVerizon 0.552832
## `prdline.my.fctriPad 1:D.nchrs.log` 0.721754
## `prdline.my.fctriPad 2:D.nchrs.log` 0.807498
## `prdline.my.fctriPad 3+:D.nchrs.log` 0.954861
## `prdline.my.fctriPadAir:D.nchrs.log` 0.047010
## `prdline.my.fctriPadmini:D.nchrs.log` 0.349405
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 0.954078
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 0.049303
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 0.028062
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 0.083171
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 0.416316
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 0.006479
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 0.369605
## `prdline.my.fctriPad 1:D.npnct16.log` 0.395902
## `prdline.my.fctriPad 2:D.npnct16.log` 0.388093
## `prdline.my.fctriPad 3+:D.npnct16.log` 0.101731
## `prdline.my.fctriPadAir:D.npnct16.log` 0.593423
## `prdline.my.fctriPadmini:D.npnct16.log` 0.144615
## `prdline.my.fctriPadmini 2+:D.npnct16.log` 0.421701
## `prdline.my.fctriPad 1:D.npnct01.log` 0.039176
## `prdline.my.fctriPad 2:D.npnct01.log` 0.071905
## `prdline.my.fctriPad 3+:D.npnct01.log` 0.015876
## `prdline.my.fctriPadAir:D.npnct01.log` 0.452167
## `prdline.my.fctriPadmini:D.npnct01.log` 0.042240
## `prdline.my.fctriPadmini 2+:D.npnct01.log` 0.160132
## `prdline.my.fctriPad 1:D.nstopwrds.log` 0.786815
## `prdline.my.fctriPad 2:D.nstopwrds.log` 0.411060
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 0.339093
## `prdline.my.fctriPadAir:D.nstopwrds.log` 0.107497
## `prdline.my.fctriPadmini:D.nstopwrds.log` 0.630280
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 0.705173
## `prdline.my.fctriPad 1:D.npnct08.log` 0.530156
## `prdline.my.fctriPad 2:D.npnct08.log` 0.436700
## `prdline.my.fctriPad 3+:D.npnct08.log` 0.793324
## `prdline.my.fctriPadAir:D.npnct08.log` 0.734497
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 0.967591
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.174650
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.125581
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.122564
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.327179
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.107128
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.849018
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 0.214479
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 0.192724
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 0.207503
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 0.371161
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 0.135473
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` 0.677948
## `prdline.my.fctriPad 1:biddable` 0.057871
## `prdline.my.fctriPad 2:biddable` 0.531483
## `prdline.my.fctriPad 3+:biddable` 0.458610
## `prdline.my.fctriPadAir:biddable` 0.002267
## `prdline.my.fctriPadmini:biddable` 0.542176
## `prdline.my.fctriPadmini 2+:biddable` 0.062612
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.926496
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.377169
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.938724
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.947098
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.967062
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.748567
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.247201
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.329878
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.249334
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.135614
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.973195
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.073007
## `prdline.my.fctriPad 1:condition.fctrNew` 0.760442
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.627007
## `prdline.my.fctriPadAir:condition.fctrNew` 0.893144
## `prdline.my.fctriPadmini:condition.fctrNew` 0.720638
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.863881
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.436465
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.636781
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.992598
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.576576
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.807302
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.207665
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.727892
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.662700
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.867138
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.367346
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.326131
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` 0.891362
## `prdline.my.fctriPadAir:color.fctrGold` 0.441265
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 0.849525
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 0.820837
## `prdline.my.fctriPadAir:color.fctrSpace Gray` 0.600612
## `prdline.my.fctriPadmini:color.fctrSpace Gray` 0.236725
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` 0.230726
## `prdline.my.fctriPad 1:color.fctrUnknown` 0.460628
## `prdline.my.fctriPad 2:color.fctrUnknown` 0.058522
## `prdline.my.fctriPad 3+:color.fctrUnknown` 0.073958
## `prdline.my.fctriPadAir:color.fctrUnknown` 0.649318
## `prdline.my.fctriPadmini:color.fctrUnknown` 0.830237
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` 0.405245
## `prdline.my.fctriPad 1:color.fctrWhite` 0.083468
## `prdline.my.fctriPad 2:color.fctrWhite` 0.047838
## `prdline.my.fctriPad 3+:color.fctrWhite` 0.129186
## `prdline.my.fctriPadAir:color.fctrWhite` 0.790964
## `prdline.my.fctriPadmini:color.fctrWhite` 0.554671
## `prdline.my.fctriPadmini 2+:color.fctrWhite` 0.415796
## `prdline.my.fctriPad 1:storage.fctr16` 0.245953
## `prdline.my.fctriPad 2:storage.fctr16` 0.886785
## `prdline.my.fctriPad 3+:storage.fctr16` 0.771576
## `prdline.my.fctriPadAir:storage.fctr16` 0.008160
## `prdline.my.fctriPadmini:storage.fctr16` 0.262594
## `prdline.my.fctriPadmini 2+:storage.fctr16` 0.007154
## `prdline.my.fctriPad 1:storage.fctr32` 0.381718
## `prdline.my.fctriPad 2:storage.fctr32` 0.086305
## `prdline.my.fctriPad 3+:storage.fctr32` 0.356852
## `prdline.my.fctriPadAir:storage.fctr32` 0.339324
## `prdline.my.fctriPadmini:storage.fctr32` 0.236477
## `prdline.my.fctriPadmini 2+:storage.fctr32` 0.827558
## `prdline.my.fctriPad 1:storage.fctr64` 0.139015
## `prdline.my.fctriPad 2:storage.fctr64` 0.661013
## `prdline.my.fctriPad 3+:storage.fctr64` 0.621041
## `prdline.my.fctriPadAir:storage.fctr64` 0.043740
## `prdline.my.fctriPadmini:storage.fctr64` 0.325587
## `prdline.my.fctriPadmini 2+:storage.fctr64` 0.050585
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 0.672459
## `prdline.my.fctriPadAir:storage.fctrUnknown` 0.000765
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` 0.778468
## `prdline.my.fctriPad 2:idseq.my` 0.698480
## `prdline.my.fctriPad 3+:idseq.my` 0.531989
## `prdline.my.fctriPadAir:idseq.my` 0.162819
## `prdline.my.fctriPadmini:idseq.my` 0.726890
## `prdline.my.fctriPadmini 2+:idseq.my` 0.046792
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.010785
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.877316
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.412115
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.485924
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.698753
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.843523
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.666740
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.360653
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.243518
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
##
## (Intercept)
## `prdline.my.fctriPad 1` .
## `prdline.my.fctriPad 2` *
## `prdline.my.fctriPad 3+` .
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini **
## `prdline.my.fctriPadmini 2+`
## D.ratio.nstopwrds.nwrds
## D.npnct14.log
## D.terms.n.stem.stop.Ratio
## D.ndgts.log
## .rnorm
## D.npnct05.log
## D.npnct15.log
## D.npnct12.log
## D.npnct06.log
## D.npnct03.log
## D.npnct11.log
## D.npnct13.log
## D.nwrds.log .
## D.terms.n.post.stop.log
## D.nwrds.unq.log
## D.terms.n.post.stem.log
## D.nuppr.log
## D.npnct24.log
## D.TfIdf.sum.post.stem
## D.sum.TfIdf
## D.TfIdf.sum.post.stop
## D.ratio.sum.TfIdf.nwrds
## D.nchrs.log
## D.TfIdf.sum.stem.stop.Ratio
## D.npnct16.log .
## D.npnct01.log *
## D.nstopwrds.log
## D.npnct08.log
## D.terms.n.post.stop
## D.terms.n.post.stem
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew .
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## color.fctrGold
## `color.fctrSpace Gray` .
## color.fctrUnknown
## color.fctrWhite *
## storage.fctr16
## storage.fctr32
## storage.fctr64
## storage.fctrUnknown
## idseq.my
## cellular.fctr1
## cellular.fctrUnknown .
## carrier.fctrNone
## carrier.fctrOther
## carrier.fctrSprint *
## `carrier.fctrT-Mobile`
## carrier.fctrUnknown
## carrier.fctrVerizon
## `prdline.my.fctriPad 1:D.nchrs.log`
## `prdline.my.fctriPad 2:D.nchrs.log`
## `prdline.my.fctriPad 3+:D.nchrs.log`
## `prdline.my.fctriPadAir:D.nchrs.log` *
## `prdline.my.fctriPadmini:D.nchrs.log`
## `prdline.my.fctriPadmini 2+:D.nchrs.log`
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` *
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` *
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` .
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` **
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 1:D.npnct16.log`
## `prdline.my.fctriPad 2:D.npnct16.log`
## `prdline.my.fctriPad 3+:D.npnct16.log`
## `prdline.my.fctriPadAir:D.npnct16.log`
## `prdline.my.fctriPadmini:D.npnct16.log`
## `prdline.my.fctriPadmini 2+:D.npnct16.log`
## `prdline.my.fctriPad 1:D.npnct01.log` *
## `prdline.my.fctriPad 2:D.npnct01.log` .
## `prdline.my.fctriPad 3+:D.npnct01.log` *
## `prdline.my.fctriPadAir:D.npnct01.log`
## `prdline.my.fctriPadmini:D.npnct01.log` *
## `prdline.my.fctriPadmini 2+:D.npnct01.log`
## `prdline.my.fctriPad 1:D.nstopwrds.log`
## `prdline.my.fctriPad 2:D.nstopwrds.log`
## `prdline.my.fctriPad 3+:D.nstopwrds.log`
## `prdline.my.fctriPadAir:D.nstopwrds.log`
## `prdline.my.fctriPadmini:D.nstopwrds.log`
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log`
## `prdline.my.fctriPad 1:D.npnct08.log`
## `prdline.my.fctriPad 2:D.npnct08.log`
## `prdline.my.fctriPad 3+:D.npnct08.log`
## `prdline.my.fctriPadAir:D.npnct08.log`
## `prdline.my.fctriPadmini:D.npnct08.log`
## `prdline.my.fctriPadmini 2+:D.npnct08.log`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:D.terms.n.post.stem`
## `prdline.my.fctriPad 2:D.terms.n.post.stem`
## `prdline.my.fctriPad 3+:D.terms.n.post.stem`
## `prdline.my.fctriPadAir:D.terms.n.post.stem`
## `prdline.my.fctriPadmini:D.terms.n.post.stem`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem`
## `prdline.my.fctriPad 1:biddable` .
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable` **
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable` .
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` .
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:color.fctrGold`
## `prdline.my.fctriPad 2:color.fctrGold`
## `prdline.my.fctriPad 3+:color.fctrGold`
## `prdline.my.fctriPadAir:color.fctrGold`
## `prdline.my.fctriPadmini:color.fctrGold`
## `prdline.my.fctriPadmini 2+:color.fctrGold`
## `prdline.my.fctriPad 1:color.fctrSpace Gray`
## `prdline.my.fctriPad 2:color.fctrSpace Gray`
## `prdline.my.fctriPad 3+:color.fctrSpace Gray`
## `prdline.my.fctriPadAir:color.fctrSpace Gray`
## `prdline.my.fctriPadmini:color.fctrSpace Gray`
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray`
## `prdline.my.fctriPad 1:color.fctrUnknown`
## `prdline.my.fctriPad 2:color.fctrUnknown` .
## `prdline.my.fctriPad 3+:color.fctrUnknown` .
## `prdline.my.fctriPadAir:color.fctrUnknown`
## `prdline.my.fctriPadmini:color.fctrUnknown`
## `prdline.my.fctriPadmini 2+:color.fctrUnknown`
## `prdline.my.fctriPad 1:color.fctrWhite` .
## `prdline.my.fctriPad 2:color.fctrWhite` *
## `prdline.my.fctriPad 3+:color.fctrWhite`
## `prdline.my.fctriPadAir:color.fctrWhite`
## `prdline.my.fctriPadmini:color.fctrWhite`
## `prdline.my.fctriPadmini 2+:color.fctrWhite`
## `prdline.my.fctriPad 1:storage.fctr16`
## `prdline.my.fctriPad 2:storage.fctr16`
## `prdline.my.fctriPad 3+:storage.fctr16`
## `prdline.my.fctriPadAir:storage.fctr16` **
## `prdline.my.fctriPadmini:storage.fctr16`
## `prdline.my.fctriPadmini 2+:storage.fctr16` **
## `prdline.my.fctriPad 1:storage.fctr32`
## `prdline.my.fctriPad 2:storage.fctr32` .
## `prdline.my.fctriPad 3+:storage.fctr32`
## `prdline.my.fctriPadAir:storage.fctr32`
## `prdline.my.fctriPadmini:storage.fctr32`
## `prdline.my.fctriPadmini 2+:storage.fctr32`
## `prdline.my.fctriPad 1:storage.fctr64`
## `prdline.my.fctriPad 2:storage.fctr64`
## `prdline.my.fctriPad 3+:storage.fctr64`
## `prdline.my.fctriPadAir:storage.fctr64` *
## `prdline.my.fctriPadmini:storage.fctr64`
## `prdline.my.fctriPadmini 2+:storage.fctr64` .
## `prdline.my.fctriPad 1:storage.fctrUnknown`
## `prdline.my.fctriPad 2:storage.fctrUnknown`
## `prdline.my.fctriPad 3+:storage.fctrUnknown`
## `prdline.my.fctriPadAir:storage.fctrUnknown` ***
## `prdline.my.fctriPadmini:storage.fctrUnknown`
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown`
## `prdline.my.fctriPad 1:idseq.my`
## `prdline.my.fctriPad 2:idseq.my`
## `prdline.my.fctriPad 3+:idseq.my`
## `prdline.my.fctriPadAir:idseq.my`
## `prdline.my.fctriPadmini:idseq.my`
## `prdline.my.fctriPadmini 2+:idseq.my` *
## `cellular.fctr1:carrier.fctrNone`
## `cellular.fctrUnknown:carrier.fctrNone`
## `cellular.fctr1:carrier.fctrOther`
## `cellular.fctrUnknown:carrier.fctrOther`
## `cellular.fctr1:carrier.fctrSprint`
## `cellular.fctrUnknown:carrier.fctrSprint`
## `cellular.fctr1:carrier.fctrT-Mobile`
## `cellular.fctrUnknown:carrier.fctrT-Mobile`
## `cellular.fctr1:carrier.fctrUnknown`
## `cellular.fctrUnknown:carrier.fctrUnknown`
## `cellular.fctr1:carrier.fctrVerizon`
## `cellular.fctrUnknown:carrier.fctrVerizon`
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80.95 on 668 degrees of freedom
## Multiple R-squared: 0.7036, Adjusted R-squared: 0.6188
## F-statistic: 8.302 on 191 and 668 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## model_id model_method
## 1 All.Interact.X.lm lm
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.4 0.132
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.7035928 109.5291 0.5299791 145.9877 0.6188416
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4185045 3.436445 0.04064351
## label step_major step_minor bgn end elapsed
## 8 fit.models_1_lm 8 0 175.673 179.268 3.595
## 9 fit.models_1_glm 9 0 179.268 NA NA
## [1] "fitting model: All.Interact.X.glm"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 64, 361, 391, 403, 435, 442, 462, 495, 532, 642, 665, 817, 824
## Warning: not plotting observations with leverage one:
## 64, 361, 391, 403, 435, 442, 462, 495, 532, 642, 665, 817, 824
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -298.79 -33.39 0.00 33.16 363.23
##
## Coefficients: (32 not defined because of singularities)
## Estimate
## (Intercept) -3.282e+04
## `prdline.my.fctriPad 1` 1.098e+03
## `prdline.my.fctriPad 2` 1.205e+03
## `prdline.my.fctriPad 3+` 1.058e+03
## prdline.my.fctriPadAir 9.484e+02
## prdline.my.fctriPadmini 1.594e+03
## `prdline.my.fctriPadmini 2+` 1.139e+03
## D.ratio.nstopwrds.nwrds -1.551e+02
## D.npnct14.log -2.551e+01
## D.terms.n.stem.stop.Ratio 3.171e+04
## D.ndgts.log 2.513e+01
## .rnorm 1.835e+00
## D.npnct05.log -9.931e+01
## D.npnct15.log 6.400e+00
## D.npnct12.log -5.514e+00
## D.npnct06.log -7.626e+01
## D.npnct03.log -6.539e+00
## D.npnct11.log -1.533e+01
## D.npnct13.log 1.976e+00
## D.nwrds.log 1.954e+02
## D.terms.n.post.stop.log 3.504e+04
## D.nwrds.unq.log -3.514e+04
## D.terms.n.post.stem.log NA
## D.nuppr.log 2.501e+02
## D.npnct24.log 3.967e+01
## D.TfIdf.sum.post.stem -4.977e+00
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 1.080e+01
## D.ratio.sum.TfIdf.nwrds -2.458e+01
## D.nchrs.log -3.177e+02
## D.TfIdf.sum.stem.stop.Ratio 1.361e+03
## D.npnct16.log 1.874e+02
## D.npnct01.log 2.994e+02
## D.nstopwrds.log -5.466e+01
## D.npnct08.log 6.453e+01
## D.terms.n.post.stop -6.808e+01
## D.terms.n.post.stem 8.859e+01
## biddable -1.178e+02
## `condition.fctrFor parts or not working` -6.056e+01
## `condition.fctrManufacturer refurbished` 7.858e+01
## condition.fctrNew 6.644e+01
## `condition.fctrNew other (see details)` 2.795e+01
## `condition.fctrSeller refurbished` -4.074e+01
## color.fctrGold -1.458e+01
## `color.fctrSpace Gray` 9.217e+01
## color.fctrUnknown 3.472e+01
## color.fctrWhite 8.049e+01
## storage.fctr16 5.563e+01
## storage.fctr32 -6.953e+01
## storage.fctr64 1.190e+02
## storage.fctrUnknown 4.559e+01
## idseq.my 1.099e-02
## cellular.fctr1 7.625e+00
## cellular.fctrUnknown -4.651e+01
## carrier.fctrNone NA
## carrier.fctrOther 6.356e+01
## carrier.fctrSprint -5.663e+01
## `carrier.fctrT-Mobile` 2.297e+01
## carrier.fctrUnknown 2.121e+01
## carrier.fctrVerizon 8.838e+00
## `prdline.my.fctriPad 1:D.nchrs.log` -9.472e+00
## `prdline.my.fctriPad 2:D.nchrs.log` -5.821e+00
## `prdline.my.fctriPad 3+:D.nchrs.log` 1.375e+00
## `prdline.my.fctriPadAir:D.nchrs.log` -4.913e+01
## `prdline.my.fctriPadmini:D.nchrs.log` -2.375e+01
## `prdline.my.fctriPadmini 2+:D.nchrs.log` -2.027e+00
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -1.103e+03
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -1.197e+03
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -9.407e+02
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` -5.274e+02
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -1.548e+03
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` -7.679e+02
## `prdline.my.fctriPad 1:D.npnct16.log` -8.155e+01
## `prdline.my.fctriPad 2:D.npnct16.log` -1.076e+02
## `prdline.my.fctriPad 3+:D.npnct16.log` -2.163e+02
## `prdline.my.fctriPadAir:D.npnct16.log` -6.338e+01
## `prdline.my.fctriPadmini:D.npnct16.log` -1.329e+02
## `prdline.my.fctriPadmini 2+:D.npnct16.log` -1.191e+02
## `prdline.my.fctriPad 1:D.npnct01.log` -3.019e+02
## `prdline.my.fctriPad 2:D.npnct01.log` -2.867e+02
## `prdline.my.fctriPad 3+:D.npnct01.log` -3.639e+02
## `prdline.my.fctriPadAir:D.npnct01.log` -1.117e+02
## `prdline.my.fctriPadmini:D.npnct01.log` -2.869e+02
## `prdline.my.fctriPadmini 2+:D.npnct01.log` -2.313e+02
## `prdline.my.fctriPad 1:D.nstopwrds.log` 1.116e+01
## `prdline.my.fctriPad 2:D.nstopwrds.log` 3.339e+01
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 3.721e+01
## `prdline.my.fctriPadAir:D.nstopwrds.log` 6.400e+01
## `prdline.my.fctriPadmini:D.nstopwrds.log` 1.997e+01
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 2.432e+01
## `prdline.my.fctriPad 1:D.npnct08.log` -8.081e+01
## `prdline.my.fctriPad 2:D.npnct08.log` -8.064e+01
## `prdline.my.fctriPad 3+:D.npnct08.log` -2.832e+01
## `prdline.my.fctriPadAir:D.npnct08.log` 4.020e+01
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` -6.096e+00
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -1.598e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.230e+02
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.195e+02
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -8.223e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.704e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 2.120e+01
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 1.446e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 1.009e+02
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 9.359e+01
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 7.301e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.572e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` -4.481e+01
## `prdline.my.fctriPad 1:biddable` 6.522e+01
## `prdline.my.fctriPad 2:biddable` 2.063e+01
## `prdline.my.fctriPad 3+:biddable` -2.495e+01
## `prdline.my.fctriPadAir:biddable` -9.896e+01
## `prdline.my.fctriPadmini:biddable` 2.004e+01
## `prdline.my.fctriPadmini 2+:biddable` -6.809e+01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 4.722e+00
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 4.368e+01
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -3.736e+00
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -3.367e+00
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 1.887e+00
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 2.712e+01
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -1.408e+02
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` -1.005e+02
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -1.245e+02
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -1.508e+02
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 3.882e+00
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -2.265e+02
## `prdline.my.fctriPad 1:condition.fctrNew` 2.799e+01
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -4.425e+01
## `prdline.my.fctriPadAir:condition.fctrNew` -5.265e+00
## `prdline.my.fctriPadmini:condition.fctrNew` -1.595e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 7.394e+00
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -8.472e+01
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -3.739e+01
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -7.181e-01
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 3.778e+01
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 1.895e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.282e+02
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 2.197e+01
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 2.749e+01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 1.075e+01
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -7.248e+01
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 7.142e+01
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` -1.867e+01
## `prdline.my.fctriPadAir:color.fctrGold` 5.798e+01
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` -1.398e+01
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 1.637e+01
## `prdline.my.fctriPadAir:color.fctrSpace Gray` -3.258e+01
## `prdline.my.fctriPadmini:color.fctrSpace Gray` -6.470e+01
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` -7.693e+01
## `prdline.my.fctriPad 1:color.fctrUnknown` -2.776e+01
## `prdline.my.fctriPad 2:color.fctrUnknown` -7.152e+01
## `prdline.my.fctriPad 3+:color.fctrUnknown` -6.751e+01
## `prdline.my.fctriPadAir:color.fctrUnknown` 2.352e+01
## `prdline.my.fctriPadmini:color.fctrUnknown` -8.298e+00
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` -4.604e+01
## `prdline.my.fctriPad 1:color.fctrWhite` -8.990e+01
## `prdline.my.fctriPad 2:color.fctrWhite` -8.756e+01
## `prdline.my.fctriPad 3+:color.fctrWhite` -6.675e+01
## `prdline.my.fctriPadAir:color.fctrWhite` -1.519e+01
## `prdline.my.fctriPadmini:color.fctrWhite` -2.824e+01
## `prdline.my.fctriPadmini 2+:color.fctrWhite` -4.720e+01
## `prdline.my.fctriPad 1:storage.fctr16` -6.196e+01
## `prdline.my.fctriPad 2:storage.fctr16` 9.371e+00
## `prdline.my.fctriPad 3+:storage.fctr16` -3.010e+01
## `prdline.my.fctriPadAir:storage.fctr16` -2.312e+02
## `prdline.my.fctriPadmini:storage.fctr16` -5.003e+01
## `prdline.my.fctriPadmini 2+:storage.fctr16` -2.102e+02
## `prdline.my.fctriPad 1:storage.fctr32` 6.326e+01
## `prdline.my.fctriPad 2:storage.fctr32` 1.440e+02
## `prdline.my.fctriPad 3+:storage.fctr32` 1.074e+02
## `prdline.my.fctriPadAir:storage.fctr32` -9.784e+01
## `prdline.my.fctriPadmini:storage.fctr32` 8.647e+01
## `prdline.my.fctriPadmini 2+:storage.fctr32` -2.104e+01
## `prdline.my.fctriPad 1:storage.fctr64` -1.106e+02
## `prdline.my.fctriPad 2:storage.fctr64` -3.832e+01
## `prdline.my.fctriPad 3+:storage.fctr64` -5.786e+01
## `prdline.my.fctriPadAir:storage.fctr64` -2.054e+02
## `prdline.my.fctriPadmini:storage.fctr64` -7.135e+01
## `prdline.my.fctriPadmini 2+:storage.fctr64` -1.879e+02
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 4.638e+01
## `prdline.my.fctriPadAir:storage.fctrUnknown` -5.528e+02
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` -1.079e-02
## `prdline.my.fctriPad 2:idseq.my` -1.496e-02
## `prdline.my.fctriPad 3+:idseq.my` -2.315e-02
## `prdline.my.fctriPadAir:idseq.my` -5.091e-02
## `prdline.my.fctriPadmini:idseq.my` -1.292e-02
## `prdline.my.fctriPadmini 2+:idseq.my` -8.014e-02
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` -1.247e+02
## `prdline.my.fctriPad 1:.clusterid.fctr2` -6.969e+00
## `prdline.my.fctriPad 2:.clusterid.fctr2` 5.321e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -4.223e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.391e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 6.355e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.186e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -4.225e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -7.546e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
## Std. Error
## (Intercept) 1.038e+05
## `prdline.my.fctriPad 1` 5.752e+02
## `prdline.my.fctriPad 2` 5.570e+02
## `prdline.my.fctriPad 3+` 5.650e+02
## prdline.my.fctriPadAir 6.689e+02
## prdline.my.fctriPadmini 5.773e+02
## `prdline.my.fctriPadmini 2+` 8.371e+02
## D.ratio.nstopwrds.nwrds 2.851e+02
## D.npnct14.log 3.985e+01
## D.terms.n.stem.stop.Ratio 1.038e+05
## D.ndgts.log 2.301e+01
## .rnorm 3.097e+00
## D.npnct05.log 8.842e+01
## D.npnct15.log 3.735e+01
## D.npnct12.log 2.707e+01
## D.npnct06.log 9.464e+01
## D.npnct03.log 6.897e+01
## D.npnct11.log 1.564e+01
## D.npnct13.log 1.654e+01
## D.nwrds.log 1.048e+02
## D.terms.n.post.stop.log 1.164e+05
## D.nwrds.unq.log 1.164e+05
## D.terms.n.post.stem.log NA
## D.nuppr.log 2.067e+02
## D.npnct24.log 2.368e+02
## D.TfIdf.sum.post.stem 1.056e+02
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 1.006e+02
## D.ratio.sum.TfIdf.nwrds 1.983e+01
## D.nchrs.log 2.390e+02
## D.TfIdf.sum.stem.stop.Ratio 8.323e+02
## D.npnct16.log 9.772e+01
## D.npnct01.log 1.363e+02
## D.nstopwrds.log 8.926e+01
## D.npnct08.log 9.575e+01
## D.terms.n.post.stop 7.377e+02
## D.terms.n.post.stem 7.394e+02
## biddable 2.680e+01
## `condition.fctrFor parts or not working` 3.678e+01
## `condition.fctrManufacturer refurbished` 9.016e+01
## condition.fctrNew 3.435e+01
## `condition.fctrNew other (see details)` 6.242e+01
## `condition.fctrSeller refurbished` 5.312e+01
## color.fctrGold 6.009e+01
## `color.fctrSpace Gray` 4.735e+01
## color.fctrUnknown 3.291e+01
## color.fctrWhite 3.964e+01
## storage.fctr16 8.338e+01
## storage.fctr32 9.843e+01
## storage.fctr64 9.833e+01
## storage.fctrUnknown 7.991e+01
## idseq.my 3.213e-02
## cellular.fctr1 1.078e+01
## cellular.fctrUnknown 2.558e+01
## carrier.fctrNone NA
## carrier.fctrOther 7.454e+01
## carrier.fctrSprint 2.851e+01
## `carrier.fctrT-Mobile` 3.345e+01
## carrier.fctrUnknown 1.739e+01
## carrier.fctrVerizon 1.488e+01
## `prdline.my.fctriPad 1:D.nchrs.log` 2.659e+01
## `prdline.my.fctriPad 2:D.nchrs.log` 2.388e+01
## `prdline.my.fctriPad 3+:D.nchrs.log` 2.428e+01
## `prdline.my.fctriPadAir:D.nchrs.log` 2.469e+01
## `prdline.my.fctriPadmini:D.nchrs.log` 2.537e+01
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 3.518e+01
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 5.599e+02
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 5.438e+02
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 5.421e+02
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 6.485e+02
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 5.667e+02
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 8.553e+02
## `prdline.my.fctriPad 1:D.npnct16.log` 9.599e+01
## `prdline.my.fctriPad 2:D.npnct16.log` 1.246e+02
## `prdline.my.fctriPad 3+:D.npnct16.log` 1.320e+02
## `prdline.my.fctriPadAir:D.npnct16.log` 1.187e+02
## `prdline.my.fctriPadmini:D.npnct16.log` 9.100e+01
## `prdline.my.fctriPadmini 2+:D.npnct16.log` 1.482e+02
## `prdline.my.fctriPad 1:D.npnct01.log` 1.461e+02
## `prdline.my.fctriPad 2:D.npnct01.log` 1.590e+02
## `prdline.my.fctriPad 3+:D.npnct01.log` 1.505e+02
## `prdline.my.fctriPadAir:D.npnct01.log` 1.485e+02
## `prdline.my.fctriPadmini:D.npnct01.log` 1.410e+02
## `prdline.my.fctriPadmini 2+:D.npnct01.log` 1.645e+02
## `prdline.my.fctriPad 1:D.nstopwrds.log` 4.126e+01
## `prdline.my.fctriPad 2:D.nstopwrds.log` 4.059e+01
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 3.889e+01
## `prdline.my.fctriPadAir:D.nstopwrds.log` 3.971e+01
## `prdline.my.fctriPadmini:D.nstopwrds.log` 4.146e+01
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 6.424e+01
## `prdline.my.fctriPad 1:D.npnct08.log` 1.287e+02
## `prdline.my.fctriPad 2:D.npnct08.log` 1.036e+02
## `prdline.my.fctriPad 3+:D.npnct08.log` 1.081e+02
## `prdline.my.fctriPadAir:D.npnct08.log` 1.185e+02
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 1.500e+02
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.176e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 8.021e+01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 7.728e+01
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 8.386e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 1.056e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 1.113e+02
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 1.164e+02
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 7.736e+01
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 7.418e+01
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 8.158e+01
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.052e+02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` 1.079e+02
## `prdline.my.fctriPad 1:biddable` 3.433e+01
## `prdline.my.fctriPad 2:biddable` 3.295e+01
## `prdline.my.fctriPad 3+:biddable` 3.365e+01
## `prdline.my.fctriPadAir:biddable` 3.229e+01
## `prdline.my.fctriPadmini:biddable` 3.287e+01
## `prdline.my.fctriPadmini 2+:biddable` 3.651e+01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 5.117e+01
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 4.943e+01
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 4.858e+01
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 5.072e+01
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 4.568e+01
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 8.457e+01
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 1.215e+02
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 1.031e+02
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 1.080e+02
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.009e+02
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 1.155e+02
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 1.261e+02
## `prdline.my.fctriPad 1:condition.fctrNew` 9.177e+01
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 9.102e+01
## `prdline.my.fctriPadAir:condition.fctrNew` 3.918e+01
## `prdline.my.fctriPadmini:condition.fctrNew` 4.459e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 4.312e+01
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 1.088e+02
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 7.915e+01
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 7.738e+01
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 6.762e+01
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 7.767e+01
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.016e+02
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 6.313e+01
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 6.300e+01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 6.424e+01
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 8.035e+01
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 7.268e+01
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` 1.366e+02
## `prdline.my.fctriPadAir:color.fctrGold` 7.525e+01
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 7.366e+01
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 7.228e+01
## `prdline.my.fctriPadAir:color.fctrSpace Gray` 6.221e+01
## `prdline.my.fctriPadmini:color.fctrSpace Gray` 5.463e+01
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` 6.413e+01
## `prdline.my.fctriPad 1:color.fctrUnknown` 3.760e+01
## `prdline.my.fctriPad 2:color.fctrUnknown` 3.774e+01
## `prdline.my.fctriPad 3+:color.fctrUnknown` 3.772e+01
## `prdline.my.fctriPadAir:color.fctrUnknown` 5.169e+01
## `prdline.my.fctriPadmini:color.fctrUnknown` 3.869e+01
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` 5.529e+01
## `prdline.my.fctriPad 1:color.fctrWhite` 5.186e+01
## `prdline.my.fctriPad 2:color.fctrWhite` 4.417e+01
## `prdline.my.fctriPad 3+:color.fctrWhite` 4.394e+01
## `prdline.my.fctriPadAir:color.fctrWhite` 5.728e+01
## `prdline.my.fctriPadmini:color.fctrWhite` 4.777e+01
## `prdline.my.fctriPadmini 2+:color.fctrWhite` 5.797e+01
## `prdline.my.fctriPad 1:storage.fctr16` 5.335e+01
## `prdline.my.fctriPad 2:storage.fctr16` 6.579e+01
## `prdline.my.fctriPad 3+:storage.fctr16` 1.036e+02
## `prdline.my.fctriPadAir:storage.fctr16` 8.712e+01
## `prdline.my.fctriPadmini:storage.fctr16` 4.462e+01
## `prdline.my.fctriPadmini 2+:storage.fctr16` 7.790e+01
## `prdline.my.fctriPad 1:storage.fctr32` 7.227e+01
## `prdline.my.fctriPad 2:storage.fctr32` 8.384e+01
## `prdline.my.fctriPad 3+:storage.fctr32` 1.165e+02
## `prdline.my.fctriPadAir:storage.fctr32` 1.023e+02
## `prdline.my.fctriPadmini:storage.fctr32` 7.298e+01
## `prdline.my.fctriPadmini 2+:storage.fctr32` 9.657e+01
## `prdline.my.fctriPad 1:storage.fctr64` 7.468e+01
## `prdline.my.fctriPad 2:storage.fctr64` 8.734e+01
## `prdline.my.fctriPad 3+:storage.fctr64` 1.170e+02
## `prdline.my.fctriPadAir:storage.fctr64` 1.017e+02
## `prdline.my.fctriPadmini:storage.fctr64` 7.253e+01
## `prdline.my.fctriPadmini 2+:storage.fctr64` 9.592e+01
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 1.097e+02
## `prdline.my.fctriPadAir:storage.fctrUnknown` 1.635e+02
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` 3.835e-02
## `prdline.my.fctriPad 2:idseq.my` 3.861e-02
## `prdline.my.fctriPad 3+:idseq.my` 3.702e-02
## `prdline.my.fctriPadAir:idseq.my` 3.644e-02
## `prdline.my.fctriPadmini:idseq.my` 3.698e-02
## `prdline.my.fctriPadmini 2+:idseq.my` 4.023e-02
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 4.878e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 4.513e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 6.483e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 6.057e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 3.594e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 3.218e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 5.073e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 4.619e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 6.465e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
## t value
## (Intercept) -0.316
## `prdline.my.fctriPad 1` 1.909
## `prdline.my.fctriPad 2` 2.164
## `prdline.my.fctriPad 3+` 1.873
## prdline.my.fctriPadAir 1.418
## prdline.my.fctriPadmini 2.761
## `prdline.my.fctriPadmini 2+` 1.360
## D.ratio.nstopwrds.nwrds -0.544
## D.npnct14.log -0.640
## D.terms.n.stem.stop.Ratio 0.305
## D.ndgts.log 1.092
## .rnorm 0.592
## D.npnct05.log -1.123
## D.npnct15.log 0.171
## D.npnct12.log -0.204
## D.npnct06.log -0.806
## D.npnct03.log -0.095
## D.npnct11.log -0.980
## D.npnct13.log 0.119
## D.nwrds.log 1.864
## D.terms.n.post.stop.log 0.301
## D.nwrds.unq.log -0.302
## D.terms.n.post.stem.log NA
## D.nuppr.log 1.210
## D.npnct24.log 0.168
## D.TfIdf.sum.post.stem -0.047
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 0.107
## D.ratio.sum.TfIdf.nwrds -1.240
## D.nchrs.log -1.329
## D.TfIdf.sum.stem.stop.Ratio 1.635
## D.npnct16.log 1.918
## D.npnct01.log 2.197
## D.nstopwrds.log -0.612
## D.npnct08.log 0.674
## D.terms.n.post.stop -0.092
## D.terms.n.post.stem 0.120
## biddable -4.396
## `condition.fctrFor parts or not working` -1.647
## `condition.fctrManufacturer refurbished` 0.872
## condition.fctrNew 1.934
## `condition.fctrNew other (see details)` 0.448
## `condition.fctrSeller refurbished` -0.767
## color.fctrGold -0.243
## `color.fctrSpace Gray` 1.947
## color.fctrUnknown 1.055
## color.fctrWhite 2.031
## storage.fctr16 0.667
## storage.fctr32 -0.706
## storage.fctr64 1.210
## storage.fctrUnknown 0.571
## idseq.my 0.342
## cellular.fctr1 0.707
## cellular.fctrUnknown -1.818
## carrier.fctrNone NA
## carrier.fctrOther 0.853
## carrier.fctrSprint -1.986
## `carrier.fctrT-Mobile` 0.687
## carrier.fctrUnknown 1.220
## carrier.fctrVerizon 0.594
## `prdline.my.fctriPad 1:D.nchrs.log` -0.356
## `prdline.my.fctriPad 2:D.nchrs.log` -0.244
## `prdline.my.fctriPad 3+:D.nchrs.log` 0.057
## `prdline.my.fctriPadAir:D.nchrs.log` -1.990
## `prdline.my.fctriPadmini:D.nchrs.log` -0.936
## `prdline.my.fctriPadmini 2+:D.nchrs.log` -0.058
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -1.970
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -2.201
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -1.735
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` -0.813
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -2.731
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` -0.898
## `prdline.my.fctriPad 1:D.npnct16.log` -0.850
## `prdline.my.fctriPad 2:D.npnct16.log` -0.864
## `prdline.my.fctriPad 3+:D.npnct16.log` -1.639
## `prdline.my.fctriPadAir:D.npnct16.log` -0.534
## `prdline.my.fctriPadmini:D.npnct16.log` -1.461
## `prdline.my.fctriPadmini 2+:D.npnct16.log` -0.804
## `prdline.my.fctriPad 1:D.npnct01.log` -2.066
## `prdline.my.fctriPad 2:D.npnct01.log` -1.803
## `prdline.my.fctriPad 3+:D.npnct01.log` -2.418
## `prdline.my.fctriPadAir:D.npnct01.log` -0.752
## `prdline.my.fctriPadmini:D.npnct01.log` -2.035
## `prdline.my.fctriPadmini 2+:D.npnct01.log` -1.406
## `prdline.my.fctriPad 1:D.nstopwrds.log` 0.271
## `prdline.my.fctriPad 2:D.nstopwrds.log` 0.823
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 0.957
## `prdline.my.fctriPadAir:D.nstopwrds.log` 1.612
## `prdline.my.fctriPadmini:D.nstopwrds.log` 0.482
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 0.379
## `prdline.my.fctriPad 1:D.npnct08.log` -0.628
## `prdline.my.fctriPad 2:D.npnct08.log` -0.778
## `prdline.my.fctriPad 3+:D.npnct08.log` -0.262
## `prdline.my.fctriPadAir:D.npnct08.log` 0.339
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` -0.041
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -1.359
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.534
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.546
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.981
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.613
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.190
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 1.243
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 1.304
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 1.262
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 0.895
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.495
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` -0.415
## `prdline.my.fctriPad 1:biddable` 1.900
## `prdline.my.fctriPad 2:biddable` 0.626
## `prdline.my.fctriPad 3+:biddable` -0.742
## `prdline.my.fctriPadAir:biddable` -3.065
## `prdline.my.fctriPadmini:biddable` 0.610
## `prdline.my.fctriPadmini 2+:biddable` -1.865
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.092
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.884
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.077
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.066
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.041
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.321
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -1.158
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` -0.975
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -1.153
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -1.494
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.034
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -1.796
## `prdline.my.fctriPad 1:condition.fctrNew` 0.305
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.486
## `prdline.my.fctriPadAir:condition.fctrNew` -0.134
## `prdline.my.fctriPadmini:condition.fctrNew` -0.358
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.172
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.779
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.472
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -0.009
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.559
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.244
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.261
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.348
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.436
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.167
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.902
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.983
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` -0.137
## `prdline.my.fctriPadAir:color.fctrGold` 0.771
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` -0.190
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 0.227
## `prdline.my.fctriPadAir:color.fctrSpace Gray` -0.524
## `prdline.my.fctriPadmini:color.fctrSpace Gray` -1.184
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` -1.200
## `prdline.my.fctriPad 1:color.fctrUnknown` -0.738
## `prdline.my.fctriPad 2:color.fctrUnknown` -1.895
## `prdline.my.fctriPad 3+:color.fctrUnknown` -1.790
## `prdline.my.fctriPadAir:color.fctrUnknown` 0.455
## `prdline.my.fctriPadmini:color.fctrUnknown` -0.214
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` -0.833
## `prdline.my.fctriPad 1:color.fctrWhite` -1.734
## `prdline.my.fctriPad 2:color.fctrWhite` -1.982
## `prdline.my.fctriPad 3+:color.fctrWhite` -1.519
## `prdline.my.fctriPadAir:color.fctrWhite` -0.265
## `prdline.my.fctriPadmini:color.fctrWhite` -0.591
## `prdline.my.fctriPadmini 2+:color.fctrWhite` -0.814
## `prdline.my.fctriPad 1:storage.fctr16` -1.161
## `prdline.my.fctriPad 2:storage.fctr16` 0.142
## `prdline.my.fctriPad 3+:storage.fctr16` -0.290
## `prdline.my.fctriPadAir:storage.fctr16` -2.653
## `prdline.my.fctriPadmini:storage.fctr16` -1.121
## `prdline.my.fctriPadmini 2+:storage.fctr16` -2.698
## `prdline.my.fctriPad 1:storage.fctr32` 0.875
## `prdline.my.fctriPad 2:storage.fctr32` 1.718
## `prdline.my.fctriPad 3+:storage.fctr32` 0.922
## `prdline.my.fctriPadAir:storage.fctr32` -0.956
## `prdline.my.fctriPadmini:storage.fctr32` 1.185
## `prdline.my.fctriPadmini 2+:storage.fctr32` -0.218
## `prdline.my.fctriPad 1:storage.fctr64` -1.481
## `prdline.my.fctriPad 2:storage.fctr64` -0.439
## `prdline.my.fctriPad 3+:storage.fctr64` -0.495
## `prdline.my.fctriPadAir:storage.fctr64` -2.020
## `prdline.my.fctriPadmini:storage.fctr64` -0.984
## `prdline.my.fctriPadmini 2+:storage.fctr64` -1.959
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 0.423
## `prdline.my.fctriPadAir:storage.fctrUnknown` -3.381
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` -0.281
## `prdline.my.fctriPad 2:idseq.my` -0.388
## `prdline.my.fctriPad 3+:idseq.my` -0.625
## `prdline.my.fctriPadAir:idseq.my` -1.397
## `prdline.my.fctriPadmini:idseq.my` -0.349
## `prdline.my.fctriPadmini 2+:idseq.my` -1.992
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` -2.557
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.154
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.821
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.697
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.387
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.197
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.431
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.915
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.167
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
## Pr(>|t|)
## (Intercept) 0.752007
## `prdline.my.fctriPad 1` 0.056750
## `prdline.my.fctriPad 2` 0.030849
## `prdline.my.fctriPad 3+` 0.061573
## prdline.my.fctriPadAir 0.156702
## prdline.my.fctriPadmini 0.005915
## `prdline.my.fctriPadmini 2+` 0.174191
## D.ratio.nstopwrds.nwrds 0.586763
## D.npnct14.log 0.522210
## D.terms.n.stem.stop.Ratio 0.760137
## D.ndgts.log 0.275222
## .rnorm 0.553779
## D.npnct05.log 0.261814
## D.npnct15.log 0.863992
## D.npnct12.log 0.838651
## D.npnct06.log 0.420674
## D.npnct03.log 0.924500
## D.npnct11.log 0.327574
## D.npnct13.log 0.904975
## D.nwrds.log 0.062744
## D.terms.n.post.stop.log 0.763494
## D.nwrds.unq.log 0.762866
## D.terms.n.post.stem.log NA
## D.nuppr.log 0.226744
## D.npnct24.log 0.867016
## D.TfIdf.sum.post.stem 0.962425
## D.sum.TfIdf NA
## D.TfIdf.sum.post.stop 0.914547
## D.ratio.sum.TfIdf.nwrds 0.215502
## D.nchrs.log 0.184336
## D.TfIdf.sum.stem.stop.Ratio 0.102433
## D.npnct16.log 0.055521
## D.npnct01.log 0.028358
## D.nstopwrds.log 0.540502
## D.npnct08.log 0.500591
## D.terms.n.post.stop 0.926501
## D.terms.n.post.stem 0.904669
## biddable 1.28e-05
## `condition.fctrFor parts or not working` 0.100124
## `condition.fctrManufacturer refurbished` 0.383768
## condition.fctrNew 0.053484
## `condition.fctrNew other (see details)` 0.654457
## `condition.fctrSeller refurbished` 0.443403
## color.fctrGold 0.808388
## `color.fctrSpace Gray` 0.051976
## color.fctrUnknown 0.291854
## color.fctrWhite 0.042671
## storage.fctr16 0.504869
## storage.fctr32 0.480148
## storage.fctr64 0.226551
## storage.fctrUnknown 0.568503
## idseq.my 0.732405
## cellular.fctr1 0.479609
## cellular.fctrUnknown 0.069493
## carrier.fctrNone NA
## carrier.fctrOther 0.394114
## carrier.fctrSprint 0.047431
## `carrier.fctrT-Mobile` 0.492575
## carrier.fctrUnknown 0.222986
## carrier.fctrVerizon 0.552832
## `prdline.my.fctriPad 1:D.nchrs.log` 0.721754
## `prdline.my.fctriPad 2:D.nchrs.log` 0.807498
## `prdline.my.fctriPad 3+:D.nchrs.log` 0.954861
## `prdline.my.fctriPadAir:D.nchrs.log` 0.047010
## `prdline.my.fctriPadmini:D.nchrs.log` 0.349405
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 0.954078
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 0.049303
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 0.028062
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 0.083171
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 0.416316
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 0.006479
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 0.369605
## `prdline.my.fctriPad 1:D.npnct16.log` 0.395902
## `prdline.my.fctriPad 2:D.npnct16.log` 0.388093
## `prdline.my.fctriPad 3+:D.npnct16.log` 0.101731
## `prdline.my.fctriPadAir:D.npnct16.log` 0.593423
## `prdline.my.fctriPadmini:D.npnct16.log` 0.144615
## `prdline.my.fctriPadmini 2+:D.npnct16.log` 0.421701
## `prdline.my.fctriPad 1:D.npnct01.log` 0.039176
## `prdline.my.fctriPad 2:D.npnct01.log` 0.071905
## `prdline.my.fctriPad 3+:D.npnct01.log` 0.015876
## `prdline.my.fctriPadAir:D.npnct01.log` 0.452167
## `prdline.my.fctriPadmini:D.npnct01.log` 0.042240
## `prdline.my.fctriPadmini 2+:D.npnct01.log` 0.160132
## `prdline.my.fctriPad 1:D.nstopwrds.log` 0.786815
## `prdline.my.fctriPad 2:D.nstopwrds.log` 0.411060
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 0.339093
## `prdline.my.fctriPadAir:D.nstopwrds.log` 0.107497
## `prdline.my.fctriPadmini:D.nstopwrds.log` 0.630280
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 0.705173
## `prdline.my.fctriPad 1:D.npnct08.log` 0.530156
## `prdline.my.fctriPad 2:D.npnct08.log` 0.436700
## `prdline.my.fctriPad 3+:D.npnct08.log` 0.793324
## `prdline.my.fctriPadAir:D.npnct08.log` 0.734497
## `prdline.my.fctriPadmini:D.npnct08.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 0.967591
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.174650
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.125581
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.122564
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.327179
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.107128
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.849018
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 0.214479
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 0.192724
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 0.207503
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 0.371161
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 0.135473
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` 0.677948
## `prdline.my.fctriPad 1:biddable` 0.057871
## `prdline.my.fctriPad 2:biddable` 0.531483
## `prdline.my.fctriPad 3+:biddable` 0.458610
## `prdline.my.fctriPadAir:biddable` 0.002267
## `prdline.my.fctriPadmini:biddable` 0.542176
## `prdline.my.fctriPadmini 2+:biddable` 0.062612
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.926496
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.377169
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.938724
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.947098
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.967062
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.748567
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.247201
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.329878
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.249334
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.135614
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.973195
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.073007
## `prdline.my.fctriPad 1:condition.fctrNew` 0.760442
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.627007
## `prdline.my.fctriPadAir:condition.fctrNew` 0.893144
## `prdline.my.fctriPadmini:condition.fctrNew` 0.720638
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.863881
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.436465
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.636781
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.992598
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.576576
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.807302
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.207665
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.727892
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.662700
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.867138
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.367346
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.326131
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` NA
## `prdline.my.fctriPad 1:color.fctrGold` NA
## `prdline.my.fctriPad 2:color.fctrGold` NA
## `prdline.my.fctriPad 3+:color.fctrGold` 0.891362
## `prdline.my.fctriPadAir:color.fctrGold` 0.441265
## `prdline.my.fctriPadmini:color.fctrGold` NA
## `prdline.my.fctriPadmini 2+:color.fctrGold` NA
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 0.849525
## `prdline.my.fctriPad 2:color.fctrSpace Gray` NA
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 0.820837
## `prdline.my.fctriPadAir:color.fctrSpace Gray` 0.600612
## `prdline.my.fctriPadmini:color.fctrSpace Gray` 0.236725
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` 0.230726
## `prdline.my.fctriPad 1:color.fctrUnknown` 0.460628
## `prdline.my.fctriPad 2:color.fctrUnknown` 0.058522
## `prdline.my.fctriPad 3+:color.fctrUnknown` 0.073958
## `prdline.my.fctriPadAir:color.fctrUnknown` 0.649318
## `prdline.my.fctriPadmini:color.fctrUnknown` 0.830237
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` 0.405245
## `prdline.my.fctriPad 1:color.fctrWhite` 0.083468
## `prdline.my.fctriPad 2:color.fctrWhite` 0.047838
## `prdline.my.fctriPad 3+:color.fctrWhite` 0.129186
## `prdline.my.fctriPadAir:color.fctrWhite` 0.790964
## `prdline.my.fctriPadmini:color.fctrWhite` 0.554671
## `prdline.my.fctriPadmini 2+:color.fctrWhite` 0.415796
## `prdline.my.fctriPad 1:storage.fctr16` 0.245953
## `prdline.my.fctriPad 2:storage.fctr16` 0.886785
## `prdline.my.fctriPad 3+:storage.fctr16` 0.771576
## `prdline.my.fctriPadAir:storage.fctr16` 0.008160
## `prdline.my.fctriPadmini:storage.fctr16` 0.262594
## `prdline.my.fctriPadmini 2+:storage.fctr16` 0.007154
## `prdline.my.fctriPad 1:storage.fctr32` 0.381718
## `prdline.my.fctriPad 2:storage.fctr32` 0.086305
## `prdline.my.fctriPad 3+:storage.fctr32` 0.356852
## `prdline.my.fctriPadAir:storage.fctr32` 0.339324
## `prdline.my.fctriPadmini:storage.fctr32` 0.236477
## `prdline.my.fctriPadmini 2+:storage.fctr32` 0.827558
## `prdline.my.fctriPad 1:storage.fctr64` 0.139015
## `prdline.my.fctriPad 2:storage.fctr64` 0.661013
## `prdline.my.fctriPad 3+:storage.fctr64` 0.621041
## `prdline.my.fctriPadAir:storage.fctr64` 0.043740
## `prdline.my.fctriPadmini:storage.fctr64` 0.325587
## `prdline.my.fctriPadmini 2+:storage.fctr64` 0.050585
## `prdline.my.fctriPad 1:storage.fctrUnknown` NA
## `prdline.my.fctriPad 2:storage.fctrUnknown` NA
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 0.672459
## `prdline.my.fctriPadAir:storage.fctrUnknown` 0.000765
## `prdline.my.fctriPadmini:storage.fctrUnknown` NA
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` NA
## `prdline.my.fctriPad 1:idseq.my` 0.778468
## `prdline.my.fctriPad 2:idseq.my` 0.698480
## `prdline.my.fctriPad 3+:idseq.my` 0.531989
## `prdline.my.fctriPadAir:idseq.my` 0.162819
## `prdline.my.fctriPadmini:idseq.my` 0.726890
## `prdline.my.fctriPadmini 2+:idseq.my` 0.046792
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.010785
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.877316
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.412115
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.485924
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.698753
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.843523
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.666740
## `prdline.my.fctrUnknown:.clusterid.fctr3` NA
## `prdline.my.fctriPad 1:.clusterid.fctr3` NA
## `prdline.my.fctriPad 2:.clusterid.fctr3` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.360653
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.243518
## `prdline.my.fctriPadmini:.clusterid.fctr3` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` NA
##
## (Intercept)
## `prdline.my.fctriPad 1` .
## `prdline.my.fctriPad 2` *
## `prdline.my.fctriPad 3+` .
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini **
## `prdline.my.fctriPadmini 2+`
## D.ratio.nstopwrds.nwrds
## D.npnct14.log
## D.terms.n.stem.stop.Ratio
## D.ndgts.log
## .rnorm
## D.npnct05.log
## D.npnct15.log
## D.npnct12.log
## D.npnct06.log
## D.npnct03.log
## D.npnct11.log
## D.npnct13.log
## D.nwrds.log .
## D.terms.n.post.stop.log
## D.nwrds.unq.log
## D.terms.n.post.stem.log
## D.nuppr.log
## D.npnct24.log
## D.TfIdf.sum.post.stem
## D.sum.TfIdf
## D.TfIdf.sum.post.stop
## D.ratio.sum.TfIdf.nwrds
## D.nchrs.log
## D.TfIdf.sum.stem.stop.Ratio
## D.npnct16.log .
## D.npnct01.log *
## D.nstopwrds.log
## D.npnct08.log
## D.terms.n.post.stop
## D.terms.n.post.stem
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew .
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## color.fctrGold
## `color.fctrSpace Gray` .
## color.fctrUnknown
## color.fctrWhite *
## storage.fctr16
## storage.fctr32
## storage.fctr64
## storage.fctrUnknown
## idseq.my
## cellular.fctr1
## cellular.fctrUnknown .
## carrier.fctrNone
## carrier.fctrOther
## carrier.fctrSprint *
## `carrier.fctrT-Mobile`
## carrier.fctrUnknown
## carrier.fctrVerizon
## `prdline.my.fctriPad 1:D.nchrs.log`
## `prdline.my.fctriPad 2:D.nchrs.log`
## `prdline.my.fctriPad 3+:D.nchrs.log`
## `prdline.my.fctriPadAir:D.nchrs.log` *
## `prdline.my.fctriPadmini:D.nchrs.log`
## `prdline.my.fctriPadmini 2+:D.nchrs.log`
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` *
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` *
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` .
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` **
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 1:D.npnct16.log`
## `prdline.my.fctriPad 2:D.npnct16.log`
## `prdline.my.fctriPad 3+:D.npnct16.log`
## `prdline.my.fctriPadAir:D.npnct16.log`
## `prdline.my.fctriPadmini:D.npnct16.log`
## `prdline.my.fctriPadmini 2+:D.npnct16.log`
## `prdline.my.fctriPad 1:D.npnct01.log` *
## `prdline.my.fctriPad 2:D.npnct01.log` .
## `prdline.my.fctriPad 3+:D.npnct01.log` *
## `prdline.my.fctriPadAir:D.npnct01.log`
## `prdline.my.fctriPadmini:D.npnct01.log` *
## `prdline.my.fctriPadmini 2+:D.npnct01.log`
## `prdline.my.fctriPad 1:D.nstopwrds.log`
## `prdline.my.fctriPad 2:D.nstopwrds.log`
## `prdline.my.fctriPad 3+:D.nstopwrds.log`
## `prdline.my.fctriPadAir:D.nstopwrds.log`
## `prdline.my.fctriPadmini:D.nstopwrds.log`
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log`
## `prdline.my.fctriPad 1:D.npnct08.log`
## `prdline.my.fctriPad 2:D.npnct08.log`
## `prdline.my.fctriPad 3+:D.npnct08.log`
## `prdline.my.fctriPadAir:D.npnct08.log`
## `prdline.my.fctriPadmini:D.npnct08.log`
## `prdline.my.fctriPadmini 2+:D.npnct08.log`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:D.terms.n.post.stem`
## `prdline.my.fctriPad 2:D.terms.n.post.stem`
## `prdline.my.fctriPad 3+:D.terms.n.post.stem`
## `prdline.my.fctriPadAir:D.terms.n.post.stem`
## `prdline.my.fctriPadmini:D.terms.n.post.stem`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem`
## `prdline.my.fctriPad 1:biddable` .
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable` **
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable` .
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` .
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:color.fctrGold`
## `prdline.my.fctriPad 2:color.fctrGold`
## `prdline.my.fctriPad 3+:color.fctrGold`
## `prdline.my.fctriPadAir:color.fctrGold`
## `prdline.my.fctriPadmini:color.fctrGold`
## `prdline.my.fctriPadmini 2+:color.fctrGold`
## `prdline.my.fctriPad 1:color.fctrSpace Gray`
## `prdline.my.fctriPad 2:color.fctrSpace Gray`
## `prdline.my.fctriPad 3+:color.fctrSpace Gray`
## `prdline.my.fctriPadAir:color.fctrSpace Gray`
## `prdline.my.fctriPadmini:color.fctrSpace Gray`
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray`
## `prdline.my.fctriPad 1:color.fctrUnknown`
## `prdline.my.fctriPad 2:color.fctrUnknown` .
## `prdline.my.fctriPad 3+:color.fctrUnknown` .
## `prdline.my.fctriPadAir:color.fctrUnknown`
## `prdline.my.fctriPadmini:color.fctrUnknown`
## `prdline.my.fctriPadmini 2+:color.fctrUnknown`
## `prdline.my.fctriPad 1:color.fctrWhite` .
## `prdline.my.fctriPad 2:color.fctrWhite` *
## `prdline.my.fctriPad 3+:color.fctrWhite`
## `prdline.my.fctriPadAir:color.fctrWhite`
## `prdline.my.fctriPadmini:color.fctrWhite`
## `prdline.my.fctriPadmini 2+:color.fctrWhite`
## `prdline.my.fctriPad 1:storage.fctr16`
## `prdline.my.fctriPad 2:storage.fctr16`
## `prdline.my.fctriPad 3+:storage.fctr16`
## `prdline.my.fctriPadAir:storage.fctr16` **
## `prdline.my.fctriPadmini:storage.fctr16`
## `prdline.my.fctriPadmini 2+:storage.fctr16` **
## `prdline.my.fctriPad 1:storage.fctr32`
## `prdline.my.fctriPad 2:storage.fctr32` .
## `prdline.my.fctriPad 3+:storage.fctr32`
## `prdline.my.fctriPadAir:storage.fctr32`
## `prdline.my.fctriPadmini:storage.fctr32`
## `prdline.my.fctriPadmini 2+:storage.fctr32`
## `prdline.my.fctriPad 1:storage.fctr64`
## `prdline.my.fctriPad 2:storage.fctr64`
## `prdline.my.fctriPad 3+:storage.fctr64`
## `prdline.my.fctriPadAir:storage.fctr64` *
## `prdline.my.fctriPadmini:storage.fctr64`
## `prdline.my.fctriPadmini 2+:storage.fctr64` .
## `prdline.my.fctriPad 1:storage.fctrUnknown`
## `prdline.my.fctriPad 2:storage.fctrUnknown`
## `prdline.my.fctriPad 3+:storage.fctrUnknown`
## `prdline.my.fctriPadAir:storage.fctrUnknown` ***
## `prdline.my.fctriPadmini:storage.fctrUnknown`
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown`
## `prdline.my.fctriPad 1:idseq.my`
## `prdline.my.fctriPad 2:idseq.my`
## `prdline.my.fctriPad 3+:idseq.my`
## `prdline.my.fctriPadAir:idseq.my`
## `prdline.my.fctriPadmini:idseq.my`
## `prdline.my.fctriPadmini 2+:idseq.my` *
## `cellular.fctr1:carrier.fctrNone`
## `cellular.fctrUnknown:carrier.fctrNone`
## `cellular.fctr1:carrier.fctrOther`
## `cellular.fctrUnknown:carrier.fctrOther`
## `cellular.fctr1:carrier.fctrSprint`
## `cellular.fctrUnknown:carrier.fctrSprint`
## `cellular.fctr1:carrier.fctrT-Mobile`
## `cellular.fctrUnknown:carrier.fctrT-Mobile`
## `cellular.fctr1:carrier.fctrUnknown`
## `cellular.fctrUnknown:carrier.fctrUnknown`
## `cellular.fctr1:carrier.fctrVerizon`
## `cellular.fctrUnknown:carrier.fctrVerizon`
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6553.142)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 4377499 on 668 degrees of freedom
## AIC: 10167
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## model_id model_method
## 1 All.Interact.X.glm glm
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.489 0.199
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.7035928 109.5291 0.5299791 145.9877 10166.72
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4185045 3.436445 0.04064351
## label step_major step_minor bgn end elapsed
## 9 fit.models_1_glm 9 0 179.268 182.858 3.591
## 10 fit.models_1_bayesglm 10 0 182.859 NA NA
## [1] "fitting model: All.Interact.X.bayesglm"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -297.95 -33.15 -0.89 33.15 365.47
##
## Coefficients:
## Estimate
## (Intercept) 129.88290
## `prdline.my.fctriPad 1` 39.99122
## `prdline.my.fctriPad 2` 154.01286
## `prdline.my.fctriPad 3+` 6.67488
## prdline.my.fctriPadAir 16.70178
## prdline.my.fctriPadmini 359.70365
## `prdline.my.fctriPadmini 2+` 113.63072
## D.ratio.nstopwrds.nwrds -95.64289
## D.npnct14.log -24.79241
## D.terms.n.stem.stop.Ratio 38.62367
## D.ndgts.log 21.90131
## .rnorm 1.84335
## D.npnct05.log -94.22091
## D.npnct15.log 2.40175
## D.npnct12.log 2.03913
## D.npnct06.log -83.82230
## D.npnct03.log -5.21075
## D.npnct11.log -18.79966
## D.npnct13.log -5.40417
## D.nwrds.log 154.42930
## D.terms.n.post.stop.log -43.87439
## D.nwrds.unq.log -15.87196
## D.terms.n.post.stem.log -15.87196
## D.nuppr.log 178.19562
## D.npnct24.log -9.95246
## D.TfIdf.sum.post.stem 17.58861
## D.sum.TfIdf 17.58861
## D.TfIdf.sum.post.stop -29.14910
## D.ratio.sum.TfIdf.nwrds -19.50613
## D.nchrs.log -224.32773
## D.TfIdf.sum.stem.stop.Ratio 137.63456
## D.npnct16.log 174.25397
## D.npnct01.log 88.74068
## D.nstopwrds.log -34.74353
## D.npnct08.log 38.58582
## D.terms.n.post.stop 58.85804
## D.terms.n.post.stem -46.73147
## biddable -126.67069
## `condition.fctrFor parts or not working` -55.39328
## `condition.fctrManufacturer refurbished` 39.52176
## condition.fctrNew 61.39187
## `condition.fctrNew other (see details)` 3.38652
## `condition.fctrSeller refurbished` -28.53403
## color.fctrGold 1.38213
## `color.fctrSpace Gray` 69.11060
## color.fctrUnknown 9.25781
## color.fctrWhite 57.06815
## storage.fctr16 -28.60905
## storage.fctr32 -144.93022
## storage.fctr64 -22.27548
## storage.fctrUnknown -40.29558
## idseq.my 0.01086
## cellular.fctr1 9.92311
## cellular.fctrUnknown -14.86463
## carrier.fctrNone 2.81373
## carrier.fctrOther 33.02679
## carrier.fctrSprint -26.10188
## `carrier.fctrT-Mobile` 14.64407
## carrier.fctrUnknown 4.37698
## carrier.fctrVerizon 4.62342
## `prdline.my.fctriPad 1:D.nchrs.log` -6.16311
## `prdline.my.fctriPad 2:D.nchrs.log` -0.70018
## `prdline.my.fctriPad 3+:D.nchrs.log` 4.38443
## `prdline.my.fctriPadAir:D.nchrs.log` -45.46533
## `prdline.my.fctriPadmini:D.nchrs.log` -17.23191
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 0.68324
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -102.46288
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -170.56470
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -6.03445
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 283.70357
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -406.76862
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 135.18970
## `prdline.my.fctriPad 1:D.npnct16.log` -58.68433
## `prdline.my.fctriPad 2:D.npnct16.log` -83.29528
## `prdline.my.fctriPad 3+:D.npnct16.log` -191.87710
## `prdline.my.fctriPadAir:D.npnct16.log` -37.95136
## `prdline.my.fctriPadmini:D.npnct16.log` -117.01800
## `prdline.my.fctriPadmini 2+:D.npnct16.log` -128.85738
## `prdline.my.fctriPad 1:D.npnct01.log` -99.71041
## `prdline.my.fctriPad 2:D.npnct01.log` -74.24368
## `prdline.my.fctriPad 3+:D.npnct01.log` -154.89212
## `prdline.my.fctriPadAir:D.npnct01.log` 87.80670
## `prdline.my.fctriPadmini:D.npnct01.log` -85.45451
## `prdline.my.fctriPadmini 2+:D.npnct01.log` -12.37486
## `prdline.my.fctriPad 1:D.nstopwrds.log` -13.22700
## `prdline.my.fctriPad 2:D.nstopwrds.log` 4.97034
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 14.00410
## `prdline.my.fctriPadAir:D.nstopwrds.log` 38.73615
## `prdline.my.fctriPadmini:D.nstopwrds.log` -4.31737
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 1.76249
## `prdline.my.fctriPad 1:D.npnct08.log` -49.95301
## `prdline.my.fctriPad 2:D.npnct08.log` -57.07151
## `prdline.my.fctriPad 3+:D.npnct08.log` -8.51244
## `prdline.my.fctriPadAir:D.npnct08.log` 62.61170
## `prdline.my.fctriPadmini:D.npnct08.log` 0.00000
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 15.69619
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -91.12899
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -78.38614
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -70.86814
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -36.66304
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -117.12044
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 57.66701
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 85.32875
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 65.53388
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 53.82031
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 35.90599
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 112.71760
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` -72.87913
## `prdline.my.fctriPad 1:biddable` 74.29128
## `prdline.my.fctriPad 2:biddable` 28.67298
## `prdline.my.fctriPad 3+:biddable` -13.82827
## `prdline.my.fctriPadAir:biddable` -90.91743
## `prdline.my.fctriPadmini:biddable` 27.21952
## `prdline.my.fctriPadmini 2+:biddable` -58.67385
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -2.42899
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 38.00988
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -10.18684
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -12.09357
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -2.04553
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 20.14149
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -99.70115
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` -62.31448
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -83.62834
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -111.23451
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 43.59137
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -183.53492
## `prdline.my.fctriPad 1:condition.fctrNew` 31.25046
## `prdline.my.fctriPad 2:condition.fctrNew` 0.00000
## `prdline.my.fctriPad 3+:condition.fctrNew` -35.89423
## `prdline.my.fctriPadAir:condition.fctrNew` -0.51365
## `prdline.my.fctriPadmini:condition.fctrNew` -9.29568
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 13.11951
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -53.15133
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -12.89233
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 25.40586
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 61.21004
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 42.07879
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 147.31612
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 10.27235
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 11.03962
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -4.34011
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -82.47110
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 68.97598
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.00000
## `prdline.my.fctriPad 1:color.fctrGold` 0.00000
## `prdline.my.fctriPad 2:color.fctrGold` 0.00000
## `prdline.my.fctriPad 3+:color.fctrGold` -30.99373
## `prdline.my.fctriPadAir:color.fctrGold` 45.27421
## `prdline.my.fctriPadmini:color.fctrGold` 0.00000
## `prdline.my.fctriPadmini 2+:color.fctrGold` -13.16095
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 7.34736
## `prdline.my.fctriPad 2:color.fctrSpace Gray` 0.00000
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 37.60017
## `prdline.my.fctriPadAir:color.fctrSpace Gray` -7.55569
## `prdline.my.fctriPadmini:color.fctrSpace Gray` -43.06464
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` -52.43771
## `prdline.my.fctriPad 1:color.fctrUnknown` -2.83493
## `prdline.my.fctriPad 2:color.fctrUnknown` -47.24305
## `prdline.my.fctriPad 3+:color.fctrUnknown` -40.76640
## `prdline.my.fctriPadAir:color.fctrUnknown` 50.97426
## `prdline.my.fctriPadmini:color.fctrUnknown` 15.43772
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` -19.20353
## `prdline.my.fctriPad 1:color.fctrWhite` -66.08033
## `prdline.my.fctriPad 2:color.fctrWhite` -65.68918
## `prdline.my.fctriPad 3+:color.fctrWhite` -42.42370
## `prdline.my.fctriPadAir:color.fctrWhite` 12.70390
## `prdline.my.fctriPadmini:color.fctrWhite` -7.95275
## `prdline.my.fctriPadmini 2+:color.fctrWhite` -22.89394
## `prdline.my.fctriPad 1:storage.fctr16` -38.01356
## `prdline.my.fctriPad 2:storage.fctr16` 3.42335
## `prdline.my.fctriPad 3+:storage.fctr16` 50.72515
## `prdline.my.fctriPadAir:storage.fctr16` -144.34078
## `prdline.my.fctriPadmini:storage.fctr16` 13.14895
## `prdline.my.fctriPadmini 2+:storage.fctr16` -124.19095
## `prdline.my.fctriPad 1:storage.fctr32` 79.41622
## `prdline.my.fctriPad 2:storage.fctr32` 129.86014
## `prdline.my.fctriPad 3+:storage.fctr32` 179.88776
## `prdline.my.fctriPadAir:storage.fctr32` -20.09259
## `prdline.my.fctriPadmini:storage.fctr32` 140.85546
## `prdline.my.fctriPadmini 2+:storage.fctr32` 55.66309
## `prdline.my.fctriPad 1:storage.fctr64` -28.98209
## `prdline.my.fctriPad 2:storage.fctr64` 11.19915
## `prdline.my.fctriPad 3+:storage.fctr64` 80.31846
## `prdline.my.fctriPadAir:storage.fctr64` -61.69555
## `prdline.my.fctriPadmini:storage.fctr64` 49.07662
## `prdline.my.fctriPadmini 2+:storage.fctr64` -43.53597
## `prdline.my.fctriPad 1:storage.fctrUnknown` 25.44417
## `prdline.my.fctriPad 2:storage.fctrUnknown` -3.01890
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 127.32882
## `prdline.my.fctriPadAir:storage.fctrUnknown` -433.25533
## `prdline.my.fctriPadmini:storage.fctrUnknown` 67.65388
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` 90.11361
## `prdline.my.fctriPad 1:idseq.my` -0.01032
## `prdline.my.fctriPad 2:idseq.my` -0.01524
## `prdline.my.fctriPad 3+:idseq.my` -0.02242
## `prdline.my.fctriPadAir:idseq.my` -0.05146
## `prdline.my.fctriPadmini:idseq.my` -0.01374
## `prdline.my.fctriPadmini 2+:idseq.my` -0.07897
## `cellular.fctr1:carrier.fctrNone` 0.00000
## `cellular.fctrUnknown:carrier.fctrNone` 0.00000
## `cellular.fctr1:carrier.fctrOther` 33.02679
## `cellular.fctrUnknown:carrier.fctrOther` 0.00000
## `cellular.fctr1:carrier.fctrSprint` -26.10188
## `cellular.fctrUnknown:carrier.fctrSprint` 0.00000
## `cellular.fctr1:carrier.fctrT-Mobile` 14.64407
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.00000
## `cellular.fctr1:carrier.fctrUnknown` 17.12342
## `cellular.fctrUnknown:carrier.fctrUnknown` -14.86463
## `cellular.fctr1:carrier.fctrVerizon` 4.62342
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.00000
## `prdline.my.fctrUnknown:.clusterid.fctr2` -113.32882
## `prdline.my.fctriPad 1:.clusterid.fctr2` -11.24539
## `prdline.my.fctriPad 2:.clusterid.fctr2` 47.63495
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -49.23149
## `prdline.my.fctriPadAir:.clusterid.fctr2` -12.59852
## `prdline.my.fctriPadmini:.clusterid.fctr2` -0.18047
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 24.86001
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.00000
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.00000
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.00000
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -37.34964
## `prdline.my.fctriPadAir:.clusterid.fctr3` -77.70377
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.00000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.00000
## Std. Error
## (Intercept) 826.98040
## `prdline.my.fctriPad 1` 315.44304
## `prdline.my.fctriPad 2` 309.31715
## `prdline.my.fctriPad 3+` 264.83612
## prdline.my.fctriPadAir 312.31204
## prdline.my.fctriPadmini 328.13372
## `prdline.my.fctriPadmini 2+` 349.46011
## D.ratio.nstopwrds.nwrds 243.26529
## D.npnct14.log 40.43294
## D.terms.n.stem.stop.Ratio 545.03441
## D.ndgts.log 22.34406
## .rnorm 3.16481
## D.npnct05.log 88.20841
## D.npnct15.log 37.69552
## D.npnct12.log 26.96275
## D.npnct06.log 92.08128
## D.npnct03.log 67.83890
## D.npnct11.log 15.35713
## D.npnct13.log 16.02932
## D.nwrds.log 97.05066
## D.terms.n.post.stop.log 450.25441
## D.nwrds.unq.log 469.78127
## D.terms.n.post.stem.log 469.78127
## D.nuppr.log 175.97933
## D.npnct24.log 207.58208
## D.TfIdf.sum.post.stem 392.94766
## D.sum.TfIdf 392.94766
## D.TfIdf.sum.post.stop 68.20261
## D.ratio.sum.TfIdf.nwrds 18.69107
## D.nchrs.log 201.19406
## D.TfIdf.sum.stem.stop.Ratio 436.73084
## D.npnct16.log 91.07310
## D.npnct01.log 95.32988
## D.nstopwrds.log 78.69934
## D.npnct08.log 86.71222
## D.terms.n.post.stop 91.86515
## D.terms.n.post.stem 90.05922
## biddable 26.58992
## `condition.fctrFor parts or not working` 36.28839
## `condition.fctrManufacturer refurbished` 82.40449
## condition.fctrNew 34.41986
## `condition.fctrNew other (see details)` 58.60897
## `condition.fctrSeller refurbished` 51.98678
## color.fctrGold 259.48942
## `color.fctrSpace Gray` 45.90292
## color.fctrUnknown 30.89199
## color.fctrWhite 38.07151
## storage.fctr16 122.88117
## storage.fctr32 127.85097
## storage.fctr64 125.80151
## storage.fctrUnknown 123.15993
## idseq.my 0.03198
## cellular.fctr1 351.55805
## cellular.fctrUnknown 469.65344
## carrier.fctrNone 351.56134
## carrier.fctrOther 393.50326
## carrier.fctrSprint 391.29069
## `carrier.fctrT-Mobile` 391.20803
## carrier.fctrUnknown 351.65418
## carrier.fctrVerizon 390.72795
## `prdline.my.fctriPad 1:D.nchrs.log` 26.06107
## `prdline.my.fctriPad 2:D.nchrs.log` 23.14208
## `prdline.my.fctriPad 3+:D.nchrs.log` 23.50352
## `prdline.my.fctriPadAir:D.nchrs.log` 23.69818
## `prdline.my.fctriPadmini:D.nchrs.log` 24.65318
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 34.14461
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 264.59906
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 253.36573
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 248.20121
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 300.84483
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 272.19257
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 348.44254
## `prdline.my.fctriPad 1:D.npnct16.log` 89.27870
## `prdline.my.fctriPad 2:D.npnct16.log` 118.60757
## `prdline.my.fctriPad 3+:D.npnct16.log` 125.01625
## `prdline.my.fctriPadAir:D.npnct16.log` 111.99898
## `prdline.my.fctriPadmini:D.npnct16.log` 85.05351
## `prdline.my.fctriPadmini 2+:D.npnct16.log` 139.45829
## `prdline.my.fctriPad 1:D.npnct01.log` 106.82619
## `prdline.my.fctriPad 2:D.npnct01.log` 124.53446
## `prdline.my.fctriPad 3+:D.npnct01.log` 114.05089
## `prdline.my.fctriPadAir:D.npnct01.log` 110.08296
## `prdline.my.fctriPadmini:D.npnct01.log` 101.76764
## `prdline.my.fctriPadmini 2+:D.npnct01.log` 129.43621
## `prdline.my.fctriPad 1:D.nstopwrds.log` 40.24132
## `prdline.my.fctriPad 2:D.nstopwrds.log` 38.68469
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 37.75541
## `prdline.my.fctriPadAir:D.nstopwrds.log` 38.65064
## `prdline.my.fctriPadmini:D.nstopwrds.log` 40.61545
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 63.29672
## `prdline.my.fctriPad 1:D.npnct08.log` 120.44981
## `prdline.my.fctriPad 2:D.npnct08.log` 95.05025
## `prdline.my.fctriPad 3+:D.npnct08.log` 99.60280
## `prdline.my.fctriPadAir:D.npnct08.log` 110.71742
## `prdline.my.fctriPadmini:D.npnct08.log` 676.57347
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 129.01547
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 107.12609
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 68.91796
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 70.43427
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 75.48766
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 91.16124
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 100.39701
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 107.02177
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 66.91908
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 68.42645
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 74.13081
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 91.67701
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` 97.80580
## `prdline.my.fctriPad 1:biddable` 34.37446
## `prdline.my.fctriPad 2:biddable` 33.04373
## `prdline.my.fctriPad 3+:biddable` 33.55455
## `prdline.my.fctriPadAir:biddable` 32.27083
## `prdline.my.fctriPadmini:biddable` 32.93397
## `prdline.my.fctriPadmini 2+:biddable` 36.60692
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 51.15145
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 49.28057
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 48.50704
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 50.67254
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 45.56366
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 83.39883
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 115.36665
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 96.22542
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 101.31883
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 93.95167
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 108.58247
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 119.62406
## `prdline.my.fctriPad 1:condition.fctrNew` 92.35756
## `prdline.my.fctriPad 2:condition.fctrNew` 676.57347
## `prdline.my.fctriPad 3+:condition.fctrNew` 91.57031
## `prdline.my.fctriPadAir:condition.fctrNew` 39.42442
## `prdline.my.fctriPadmini:condition.fctrNew` 44.99827
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 43.34177
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 106.07566
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 76.33275
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 74.37616
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 64.22844
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 74.49273
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 98.26882
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 62.36180
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 61.64288
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 63.40273
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 79.60125
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 72.00528
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 676.57347
## `prdline.my.fctriPad 1:color.fctrGold` 676.57347
## `prdline.my.fctriPad 2:color.fctrGold` 676.57347
## `prdline.my.fctriPad 3+:color.fctrGold` 272.62801
## `prdline.my.fctriPadAir:color.fctrGold` 260.98921
## `prdline.my.fctriPadmini:color.fctrGold` 676.57347
## `prdline.my.fctriPadmini 2+:color.fctrGold` 263.02640
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 73.17230
## `prdline.my.fctriPad 2:color.fctrSpace Gray` 676.57347
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 71.71416
## `prdline.my.fctriPadAir:color.fctrSpace Gray` 60.88308
## `prdline.my.fctriPadmini:color.fctrSpace Gray` 53.52017
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` 62.95409
## `prdline.my.fctriPad 1:color.fctrUnknown` 35.95312
## `prdline.my.fctriPad 2:color.fctrUnknown` 36.28331
## `prdline.my.fctriPad 3+:color.fctrUnknown` 36.05472
## `prdline.my.fctriPadAir:color.fctrUnknown` 50.19522
## `prdline.my.fctriPadmini:color.fctrUnknown` 37.19299
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` 54.33219
## `prdline.my.fctriPad 1:color.fctrWhite` 50.90194
## `prdline.my.fctriPad 2:color.fctrWhite` 42.86484
## `prdline.my.fctriPad 3+:color.fctrWhite` 42.52635
## `prdline.my.fctriPadAir:color.fctrWhite` 56.03545
## `prdline.my.fctriPadmini:color.fctrWhite` 46.63797
## `prdline.my.fctriPadmini 2+:color.fctrWhite` 56.78395
## `prdline.my.fctriPad 1:storage.fctr16` 236.37698
## `prdline.my.fctriPad 2:storage.fctr16` 236.77141
## `prdline.my.fctriPad 3+:storage.fctr16` 129.88151
## `prdline.my.fctriPadAir:storage.fctr16` 124.28252
## `prdline.my.fctriPadmini:storage.fctr16` 240.46777
## `prdline.my.fctriPadmini 2+:storage.fctr16` 125.91772
## `prdline.my.fctriPad 1:storage.fctr32` 239.09325
## `prdline.my.fctriPad 2:storage.fctr32` 239.61244
## `prdline.my.fctriPad 3+:storage.fctr32` 134.99870
## `prdline.my.fctriPadAir:storage.fctr32` 129.98665
## `prdline.my.fctriPadmini:storage.fctr32` 243.87055
## `prdline.my.fctriPadmini 2+:storage.fctr32` 133.06168
## `prdline.my.fctriPad 1:storage.fctr64` 237.46879
## `prdline.my.fctriPad 2:storage.fctr64` 238.96241
## `prdline.my.fctriPad 3+:storage.fctr64` 133.19465
## `prdline.my.fctriPadAir:storage.fctr64` 127.65163
## `prdline.my.fctriPadmini:storage.fctr64` 242.60319
## `prdline.my.fctriPadmini 2+:storage.fctr64` 130.29035
## `prdline.my.fctriPad 1:storage.fctrUnknown` 238.79363
## `prdline.my.fctriPad 2:storage.fctrUnknown` 241.20777
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 135.55649
## `prdline.my.fctriPadAir:storage.fctrUnknown` 180.60655
## `prdline.my.fctriPadmini:storage.fctrUnknown` 241.66882
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` 139.08295
## `prdline.my.fctriPad 1:idseq.my` 0.03846
## `prdline.my.fctriPad 2:idseq.my` 0.03847
## `prdline.my.fctriPad 3+:idseq.my` 0.03704
## `prdline.my.fctriPadAir:idseq.my` 0.03643
## `prdline.my.fctriPadmini:idseq.my` 0.03703
## `prdline.my.fctriPadmini 2+:idseq.my` 0.04036
## `cellular.fctr1:carrier.fctrNone` 676.57347
## `cellular.fctrUnknown:carrier.fctrNone` 676.57347
## `cellular.fctr1:carrier.fctrOther` 393.50326
## `cellular.fctrUnknown:carrier.fctrOther` 676.57347
## `cellular.fctr1:carrier.fctrSprint` 391.29069
## `cellular.fctrUnknown:carrier.fctrSprint` 676.57347
## `cellular.fctr1:carrier.fctrT-Mobile` 391.20803
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 676.57347
## `cellular.fctr1:carrier.fctrUnknown` 351.69910
## `cellular.fctrUnknown:carrier.fctrUnknown` 469.65344
## `cellular.fctr1:carrier.fctrVerizon` 390.72795
## `cellular.fctrUnknown:carrier.fctrVerizon` 676.57347
## `prdline.my.fctrUnknown:.clusterid.fctr2` 48.61874
## `prdline.my.fctriPad 1:.clusterid.fctr2` 45.92688
## `prdline.my.fctriPad 2:.clusterid.fctr2` 65.31138
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 61.19999
## `prdline.my.fctriPadAir:.clusterid.fctr2` 36.40058
## `prdline.my.fctriPadmini:.clusterid.fctr2` 32.61657
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 51.44863
## `prdline.my.fctrUnknown:.clusterid.fctr3` 676.57347
## `prdline.my.fctriPad 1:.clusterid.fctr3` 676.57347
## `prdline.my.fctriPad 2:.clusterid.fctr3` 676.57347
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 46.71533
## `prdline.my.fctriPadAir:.clusterid.fctr3` 64.67025
## `prdline.my.fctriPadmini:.clusterid.fctr3` 676.57347
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 676.57347
## t value
## (Intercept) 0.157
## `prdline.my.fctriPad 1` 0.127
## `prdline.my.fctriPad 2` 0.498
## `prdline.my.fctriPad 3+` 0.025
## prdline.my.fctriPadAir 0.053
## prdline.my.fctriPadmini 1.096
## `prdline.my.fctriPadmini 2+` 0.325
## D.ratio.nstopwrds.nwrds -0.393
## D.npnct14.log -0.613
## D.terms.n.stem.stop.Ratio 0.071
## D.ndgts.log 0.980
## .rnorm 0.582
## D.npnct05.log -1.068
## D.npnct15.log 0.064
## D.npnct12.log 0.076
## D.npnct06.log -0.910
## D.npnct03.log -0.077
## D.npnct11.log -1.224
## D.npnct13.log -0.337
## D.nwrds.log 1.591
## D.terms.n.post.stop.log -0.097
## D.nwrds.unq.log -0.034
## D.terms.n.post.stem.log -0.034
## D.nuppr.log 1.013
## D.npnct24.log -0.048
## D.TfIdf.sum.post.stem 0.045
## D.sum.TfIdf 0.045
## D.TfIdf.sum.post.stop -0.427
## D.ratio.sum.TfIdf.nwrds -1.044
## D.nchrs.log -1.115
## D.TfIdf.sum.stem.stop.Ratio 0.315
## D.npnct16.log 1.913
## D.npnct01.log 0.931
## D.nstopwrds.log -0.441
## D.npnct08.log 0.445
## D.terms.n.post.stop 0.641
## D.terms.n.post.stem -0.519
## biddable -4.764
## `condition.fctrFor parts or not working` -1.526
## `condition.fctrManufacturer refurbished` 0.480
## condition.fctrNew 1.784
## `condition.fctrNew other (see details)` 0.058
## `condition.fctrSeller refurbished` -0.549
## color.fctrGold 0.005
## `color.fctrSpace Gray` 1.506
## color.fctrUnknown 0.300
## color.fctrWhite 1.499
## storage.fctr16 -0.233
## storage.fctr32 -1.134
## storage.fctr64 -0.177
## storage.fctrUnknown -0.327
## idseq.my 0.339
## cellular.fctr1 0.028
## cellular.fctrUnknown -0.032
## carrier.fctrNone 0.008
## carrier.fctrOther 0.084
## carrier.fctrSprint -0.067
## `carrier.fctrT-Mobile` 0.037
## carrier.fctrUnknown 0.012
## carrier.fctrVerizon 0.012
## `prdline.my.fctriPad 1:D.nchrs.log` -0.236
## `prdline.my.fctriPad 2:D.nchrs.log` -0.030
## `prdline.my.fctriPad 3+:D.nchrs.log` 0.187
## `prdline.my.fctriPadAir:D.nchrs.log` -1.919
## `prdline.my.fctriPadmini:D.nchrs.log` -0.699
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 0.020
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -0.387
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -0.673
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -0.024
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 0.943
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -1.494
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 0.388
## `prdline.my.fctriPad 1:D.npnct16.log` -0.657
## `prdline.my.fctriPad 2:D.npnct16.log` -0.702
## `prdline.my.fctriPad 3+:D.npnct16.log` -1.535
## `prdline.my.fctriPadAir:D.npnct16.log` -0.339
## `prdline.my.fctriPadmini:D.npnct16.log` -1.376
## `prdline.my.fctriPadmini 2+:D.npnct16.log` -0.924
## `prdline.my.fctriPad 1:D.npnct01.log` -0.933
## `prdline.my.fctriPad 2:D.npnct01.log` -0.596
## `prdline.my.fctriPad 3+:D.npnct01.log` -1.358
## `prdline.my.fctriPadAir:D.npnct01.log` 0.798
## `prdline.my.fctriPadmini:D.npnct01.log` -0.840
## `prdline.my.fctriPadmini 2+:D.npnct01.log` -0.096
## `prdline.my.fctriPad 1:D.nstopwrds.log` -0.329
## `prdline.my.fctriPad 2:D.nstopwrds.log` 0.128
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 0.371
## `prdline.my.fctriPadAir:D.nstopwrds.log` 1.002
## `prdline.my.fctriPadmini:D.nstopwrds.log` -0.106
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 0.028
## `prdline.my.fctriPad 1:D.npnct08.log` -0.415
## `prdline.my.fctriPad 2:D.npnct08.log` -0.600
## `prdline.my.fctriPad 3+:D.npnct08.log` -0.085
## `prdline.my.fctriPadAir:D.npnct08.log` 0.566
## `prdline.my.fctriPadmini:D.npnct08.log` 0.000
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 0.122
## `prdline.my.fctriPad 1:D.terms.n.post.stop` -0.851
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.137
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.006
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.486
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.285
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.574
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 0.797
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 0.979
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 0.787
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 0.484
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 1.230
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` -0.745
## `prdline.my.fctriPad 1:biddable` 2.161
## `prdline.my.fctriPad 2:biddable` 0.868
## `prdline.my.fctriPad 3+:biddable` -0.412
## `prdline.my.fctriPadAir:biddable` -2.817
## `prdline.my.fctriPadmini:biddable` 0.826
## `prdline.my.fctriPadmini 2+:biddable` -1.603
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -0.047
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.771
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.210
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.239
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -0.045
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.242
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.864
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` -0.648
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.825
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -1.184
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.401
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -1.534
## `prdline.my.fctriPad 1:condition.fctrNew` 0.338
## `prdline.my.fctriPad 2:condition.fctrNew` 0.000
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.392
## `prdline.my.fctriPadAir:condition.fctrNew` -0.013
## `prdline.my.fctriPadmini:condition.fctrNew` -0.207
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.303
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.501
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.169
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.342
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.953
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.565
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.499
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.165
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.179
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.068
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -1.036
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.958
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.000
## `prdline.my.fctriPad 1:color.fctrGold` 0.000
## `prdline.my.fctriPad 2:color.fctrGold` 0.000
## `prdline.my.fctriPad 3+:color.fctrGold` -0.114
## `prdline.my.fctriPadAir:color.fctrGold` 0.173
## `prdline.my.fctriPadmini:color.fctrGold` 0.000
## `prdline.my.fctriPadmini 2+:color.fctrGold` -0.050
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 0.100
## `prdline.my.fctriPad 2:color.fctrSpace Gray` 0.000
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 0.524
## `prdline.my.fctriPadAir:color.fctrSpace Gray` -0.124
## `prdline.my.fctriPadmini:color.fctrSpace Gray` -0.805
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` -0.833
## `prdline.my.fctriPad 1:color.fctrUnknown` -0.079
## `prdline.my.fctriPad 2:color.fctrUnknown` -1.302
## `prdline.my.fctriPad 3+:color.fctrUnknown` -1.131
## `prdline.my.fctriPadAir:color.fctrUnknown` 1.016
## `prdline.my.fctriPadmini:color.fctrUnknown` 0.415
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` -0.353
## `prdline.my.fctriPad 1:color.fctrWhite` -1.298
## `prdline.my.fctriPad 2:color.fctrWhite` -1.532
## `prdline.my.fctriPad 3+:color.fctrWhite` -0.998
## `prdline.my.fctriPadAir:color.fctrWhite` 0.227
## `prdline.my.fctriPadmini:color.fctrWhite` -0.171
## `prdline.my.fctriPadmini 2+:color.fctrWhite` -0.403
## `prdline.my.fctriPad 1:storage.fctr16` -0.161
## `prdline.my.fctriPad 2:storage.fctr16` 0.014
## `prdline.my.fctriPad 3+:storage.fctr16` 0.391
## `prdline.my.fctriPadAir:storage.fctr16` -1.161
## `prdline.my.fctriPadmini:storage.fctr16` 0.055
## `prdline.my.fctriPadmini 2+:storage.fctr16` -0.986
## `prdline.my.fctriPad 1:storage.fctr32` 0.332
## `prdline.my.fctriPad 2:storage.fctr32` 0.542
## `prdline.my.fctriPad 3+:storage.fctr32` 1.333
## `prdline.my.fctriPadAir:storage.fctr32` -0.155
## `prdline.my.fctriPadmini:storage.fctr32` 0.578
## `prdline.my.fctriPadmini 2+:storage.fctr32` 0.418
## `prdline.my.fctriPad 1:storage.fctr64` -0.122
## `prdline.my.fctriPad 2:storage.fctr64` 0.047
## `prdline.my.fctriPad 3+:storage.fctr64` 0.603
## `prdline.my.fctriPadAir:storage.fctr64` -0.483
## `prdline.my.fctriPadmini:storage.fctr64` 0.202
## `prdline.my.fctriPadmini 2+:storage.fctr64` -0.334
## `prdline.my.fctriPad 1:storage.fctrUnknown` 0.107
## `prdline.my.fctriPad 2:storage.fctrUnknown` -0.013
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 0.939
## `prdline.my.fctriPadAir:storage.fctrUnknown` -2.399
## `prdline.my.fctriPadmini:storage.fctrUnknown` 0.280
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` 0.648
## `prdline.my.fctriPad 1:idseq.my` -0.268
## `prdline.my.fctriPad 2:idseq.my` -0.396
## `prdline.my.fctriPad 3+:idseq.my` -0.605
## `prdline.my.fctriPadAir:idseq.my` -1.413
## `prdline.my.fctriPadmini:idseq.my` -0.371
## `prdline.my.fctriPadmini 2+:idseq.my` -1.957
## `cellular.fctr1:carrier.fctrNone` 0.000
## `cellular.fctrUnknown:carrier.fctrNone` 0.000
## `cellular.fctr1:carrier.fctrOther` 0.084
## `cellular.fctrUnknown:carrier.fctrOther` 0.000
## `cellular.fctr1:carrier.fctrSprint` -0.067
## `cellular.fctrUnknown:carrier.fctrSprint` 0.000
## `cellular.fctr1:carrier.fctrT-Mobile` 0.037
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.000
## `cellular.fctr1:carrier.fctrUnknown` 0.049
## `cellular.fctrUnknown:carrier.fctrUnknown` -0.032
## `cellular.fctr1:carrier.fctrVerizon` 0.012
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr2` -2.331
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.245
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.729
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.804
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.346
## `prdline.my.fctriPadmini:.clusterid.fctr2` -0.006
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.483
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.000
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.800
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.202
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.000
## Pr(>|t|)
## (Intercept) 0.87525
## `prdline.my.fctriPad 1` 0.89916
## `prdline.my.fctriPad 2` 0.61872
## `prdline.my.fctriPad 3+` 0.97990
## prdline.my.fctriPadAir 0.95737
## prdline.my.fctriPadmini 0.27340
## `prdline.my.fctriPadmini 2+` 0.74517
## D.ratio.nstopwrds.nwrds 0.69433
## D.npnct14.log 0.53998
## D.terms.n.stem.stop.Ratio 0.94353
## D.ndgts.log 0.32737
## .rnorm 0.56047
## D.npnct05.log 0.28585
## D.npnct15.log 0.94922
## D.npnct12.log 0.93974
## D.npnct06.log 0.36301
## D.npnct03.log 0.93880
## D.npnct11.log 0.22134
## D.npnct13.log 0.73612
## D.nwrds.log 0.11206
## D.terms.n.post.stop.log 0.92240
## D.nwrds.unq.log 0.97306
## D.terms.n.post.stem.log 0.97306
## D.nuppr.log 0.31164
## D.npnct24.log 0.96178
## D.TfIdf.sum.post.stem 0.96431
## D.sum.TfIdf 0.96431
## D.TfIdf.sum.post.stop 0.66924
## D.ratio.sum.TfIdf.nwrds 0.29706
## D.nchrs.log 0.26528
## D.TfIdf.sum.stem.stop.Ratio 0.75275
## D.npnct16.log 0.05615
## D.npnct01.log 0.35227
## D.nstopwrds.log 0.65902
## D.npnct08.log 0.65648
## D.terms.n.post.stop 0.52195
## D.terms.n.post.stem 0.60401
## biddable 2.35e-06
## `condition.fctrFor parts or not working` 0.12739
## `condition.fctrManufacturer refurbished` 0.63167
## condition.fctrNew 0.07496
## `condition.fctrNew other (see details)` 0.95394
## `condition.fctrSeller refurbished` 0.58329
## color.fctrGold 0.99575
## `color.fctrSpace Gray` 0.13267
## color.fctrUnknown 0.76452
## color.fctrWhite 0.13438
## storage.fctr16 0.81598
## storage.fctr32 0.25739
## storage.fctr64 0.85951
## storage.fctrUnknown 0.74364
## idseq.my 0.73436
## cellular.fctr1 0.97749
## cellular.fctrUnknown 0.97476
## carrier.fctrNone 0.99362
## carrier.fctrOther 0.93314
## carrier.fctrSprint 0.94684
## `carrier.fctrT-Mobile` 0.97015
## carrier.fctrUnknown 0.99007
## carrier.fctrVerizon 0.99056
## `prdline.my.fctriPad 1:D.nchrs.log` 0.81313
## `prdline.my.fctriPad 2:D.nchrs.log` 0.97587
## `prdline.my.fctriPad 3+:D.nchrs.log` 0.85208
## `prdline.my.fctriPadAir:D.nchrs.log` 0.05549
## `prdline.my.fctriPadmini:D.nchrs.log` 0.48482
## `prdline.my.fctriPadmini 2+:D.nchrs.log` 0.98404
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 0.69871
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 0.50107
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 0.98061
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 0.34603
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 0.13556
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 0.69816
## `prdline.my.fctriPad 1:D.npnct16.log` 0.51122
## `prdline.my.fctriPad 2:D.npnct16.log` 0.48276
## `prdline.my.fctriPad 3+:D.npnct16.log` 0.12533
## `prdline.my.fctriPadAir:D.npnct16.log` 0.73483
## `prdline.my.fctriPadmini:D.npnct16.log` 0.16936
## `prdline.my.fctriPadmini 2+:D.npnct16.log` 0.35584
## `prdline.my.fctriPad 1:D.npnct01.log` 0.35097
## `prdline.my.fctriPad 2:D.npnct01.log` 0.55127
## `prdline.my.fctriPad 3+:D.npnct01.log` 0.17491
## `prdline.my.fctriPadAir:D.npnct01.log` 0.42538
## `prdline.my.fctriPadmini:D.npnct01.log` 0.40139
## `prdline.my.fctriPadmini 2+:D.npnct01.log` 0.92386
## `prdline.my.fctriPad 1:D.nstopwrds.log` 0.74250
## `prdline.my.fctriPad 2:D.nstopwrds.log` 0.89781
## `prdline.my.fctriPad 3+:D.nstopwrds.log` 0.71082
## `prdline.my.fctriPadAir:D.nstopwrds.log` 0.31662
## `prdline.my.fctriPadmini:D.nstopwrds.log` 0.91538
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log` 0.97779
## `prdline.my.fctriPad 1:D.npnct08.log` 0.67849
## `prdline.my.fctriPad 2:D.npnct08.log` 0.54843
## `prdline.my.fctriPad 3+:D.npnct08.log` 0.93192
## `prdline.my.fctriPadAir:D.npnct08.log` 0.57193
## `prdline.my.fctriPadmini:D.npnct08.log` 1.00000
## `prdline.my.fctriPadmini 2+:D.npnct08.log` 0.90321
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.39527
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.25581
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.31472
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.62736
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.19934
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.56591
## `prdline.my.fctriPad 1:D.terms.n.post.stem` 0.42557
## `prdline.my.fctriPad 2:D.terms.n.post.stem` 0.32780
## `prdline.my.fctriPad 3+:D.terms.n.post.stem` 0.43184
## `prdline.my.fctriPadAir:D.terms.n.post.stem` 0.62830
## `prdline.my.fctriPadmini:D.terms.n.post.stem` 0.21934
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem` 0.45646
## `prdline.my.fctriPad 1:biddable` 0.03105
## `prdline.my.fctriPad 2:biddable` 0.38587
## `prdline.my.fctriPad 3+:biddable` 0.68040
## `prdline.my.fctriPadAir:biddable` 0.00499
## `prdline.my.fctriPadmini:biddable` 0.40884
## `prdline.my.fctriPadmini 2+:biddable` 0.10947
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.96214
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.44082
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.83373
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.81145
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.96421
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.80924
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.38780
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.51748
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.40946
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.23687
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.68822
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.12546
## `prdline.my.fctriPad 1:condition.fctrNew` 0.73520
## `prdline.my.fctriPad 2:condition.fctrNew` 1.00000
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.69520
## `prdline.my.fctriPadAir:condition.fctrNew` 0.98961
## `prdline.my.fctriPadmini:condition.fctrNew` 0.83641
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.76222
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.61650
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.86593
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.73278
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.34095
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.57236
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.13434
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.86922
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.85792
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.94545
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.30057
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.33846
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 1.00000
## `prdline.my.fctriPad 1:color.fctrGold` 1.00000
## `prdline.my.fctriPad 2:color.fctrGold` 1.00000
## `prdline.my.fctriPad 3+:color.fctrGold` 0.90952
## `prdline.my.fctriPadAir:color.fctrGold` 0.86234
## `prdline.my.fctriPadmini:color.fctrGold` 1.00000
## `prdline.my.fctriPadmini 2+:color.fctrGold` 0.96011
## `prdline.my.fctriPad 1:color.fctrSpace Gray` 0.92005
## `prdline.my.fctriPad 2:color.fctrSpace Gray` 1.00000
## `prdline.my.fctriPad 3+:color.fctrSpace Gray` 0.60025
## `prdline.my.fctriPadAir:color.fctrSpace Gray` 0.90127
## `prdline.my.fctriPadmini:color.fctrSpace Gray` 0.42133
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray` 0.40518
## `prdline.my.fctriPad 1:color.fctrUnknown` 0.93718
## `prdline.my.fctriPad 2:color.fctrUnknown` 0.19337
## `prdline.my.fctriPad 3+:color.fctrUnknown` 0.25862
## `prdline.my.fctriPadAir:color.fctrUnknown` 0.31024
## `prdline.my.fctriPadmini:color.fctrUnknown` 0.67823
## `prdline.my.fctriPadmini 2+:color.fctrUnknown` 0.72387
## `prdline.my.fctriPad 1:color.fctrWhite` 0.19469
## `prdline.my.fctriPad 2:color.fctrWhite` 0.12590
## `prdline.my.fctriPad 3+:color.fctrWhite` 0.31886
## `prdline.my.fctriPadAir:color.fctrWhite` 0.82072
## `prdline.my.fctriPadmini:color.fctrWhite` 0.86465
## `prdline.my.fctriPadmini 2+:color.fctrWhite` 0.68695
## `prdline.my.fctriPad 1:storage.fctr16` 0.87229
## `prdline.my.fctriPad 2:storage.fctr16` 0.98847
## `prdline.my.fctriPad 3+:storage.fctr16` 0.69626
## `prdline.my.fctriPadAir:storage.fctr16` 0.24592
## `prdline.my.fctriPadmini:storage.fctr16` 0.95641
## `prdline.my.fctriPadmini 2+:storage.fctr16` 0.32437
## `prdline.my.fctriPad 1:storage.fctr32` 0.73988
## `prdline.my.fctriPad 2:storage.fctr32` 0.58804
## `prdline.my.fctriPad 3+:storage.fctr32` 0.18317
## `prdline.my.fctriPadAir:storage.fctr32` 0.87721
## `prdline.my.fctriPadmini:storage.fctr32` 0.56375
## `prdline.my.fctriPadmini 2+:storage.fctr32` 0.67585
## `prdline.my.fctriPad 1:storage.fctr64` 0.90290
## `prdline.my.fctriPad 2:storage.fctr64` 0.96263
## `prdline.my.fctriPad 3+:storage.fctr64` 0.54671
## `prdline.my.fctriPadAir:storage.fctr64` 0.62904
## `prdline.my.fctriPadmini:storage.fctr64` 0.83975
## `prdline.my.fctriPadmini 2+:storage.fctr64` 0.73838
## `prdline.my.fctriPad 1:storage.fctrUnknown` 0.91518
## `prdline.my.fctriPad 2:storage.fctrUnknown` 0.99002
## `prdline.my.fctriPad 3+:storage.fctrUnknown` 0.34793
## `prdline.my.fctriPadAir:storage.fctrUnknown` 0.01673
## `prdline.my.fctriPadmini:storage.fctrUnknown` 0.77961
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown` 0.51728
## `prdline.my.fctriPad 1:idseq.my` 0.78862
## `prdline.my.fctriPad 2:idseq.my` 0.69219
## `prdline.my.fctriPad 3+:idseq.my` 0.54525
## `prdline.my.fctriPadAir:idseq.my` 0.15826
## `prdline.my.fctriPadmini:idseq.my` 0.71070
## `prdline.my.fctriPadmini 2+:idseq.my` 0.05084
## `cellular.fctr1:carrier.fctrNone` 1.00000
## `cellular.fctrUnknown:carrier.fctrNone` 1.00000
## `cellular.fctr1:carrier.fctrOther` 0.93314
## `cellular.fctrUnknown:carrier.fctrOther` 1.00000
## `cellular.fctr1:carrier.fctrSprint` 0.94684
## `cellular.fctrUnknown:carrier.fctrSprint` 1.00000
## `cellular.fctr1:carrier.fctrT-Mobile` 0.97015
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 1.00000
## `cellular.fctr1:carrier.fctrUnknown` 0.96118
## `cellular.fctrUnknown:carrier.fctrUnknown` 0.97476
## `cellular.fctr1:carrier.fctrVerizon` 0.99056
## `cellular.fctrUnknown:carrier.fctrVerizon` 1.00000
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.02007
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.80665
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.46606
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.42145
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.72938
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.99559
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.62912
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.00000
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.00000
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.00000
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.42429
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.22999
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.00000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.00000
##
## (Intercept)
## `prdline.my.fctriPad 1`
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## D.ratio.nstopwrds.nwrds
## D.npnct14.log
## D.terms.n.stem.stop.Ratio
## D.ndgts.log
## .rnorm
## D.npnct05.log
## D.npnct15.log
## D.npnct12.log
## D.npnct06.log
## D.npnct03.log
## D.npnct11.log
## D.npnct13.log
## D.nwrds.log
## D.terms.n.post.stop.log
## D.nwrds.unq.log
## D.terms.n.post.stem.log
## D.nuppr.log
## D.npnct24.log
## D.TfIdf.sum.post.stem
## D.sum.TfIdf
## D.TfIdf.sum.post.stop
## D.ratio.sum.TfIdf.nwrds
## D.nchrs.log
## D.TfIdf.sum.stem.stop.Ratio
## D.npnct16.log .
## D.npnct01.log
## D.nstopwrds.log
## D.npnct08.log
## D.terms.n.post.stop
## D.terms.n.post.stem
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew .
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## color.fctrGold
## `color.fctrSpace Gray`
## color.fctrUnknown
## color.fctrWhite
## storage.fctr16
## storage.fctr32
## storage.fctr64
## storage.fctrUnknown
## idseq.my
## cellular.fctr1
## cellular.fctrUnknown
## carrier.fctrNone
## carrier.fctrOther
## carrier.fctrSprint
## `carrier.fctrT-Mobile`
## carrier.fctrUnknown
## carrier.fctrVerizon
## `prdline.my.fctriPad 1:D.nchrs.log`
## `prdline.my.fctriPad 2:D.nchrs.log`
## `prdline.my.fctriPad 3+:D.nchrs.log`
## `prdline.my.fctriPadAir:D.nchrs.log` .
## `prdline.my.fctriPadmini:D.nchrs.log`
## `prdline.my.fctriPadmini 2+:D.nchrs.log`
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 1:D.npnct16.log`
## `prdline.my.fctriPad 2:D.npnct16.log`
## `prdline.my.fctriPad 3+:D.npnct16.log`
## `prdline.my.fctriPadAir:D.npnct16.log`
## `prdline.my.fctriPadmini:D.npnct16.log`
## `prdline.my.fctriPadmini 2+:D.npnct16.log`
## `prdline.my.fctriPad 1:D.npnct01.log`
## `prdline.my.fctriPad 2:D.npnct01.log`
## `prdline.my.fctriPad 3+:D.npnct01.log`
## `prdline.my.fctriPadAir:D.npnct01.log`
## `prdline.my.fctriPadmini:D.npnct01.log`
## `prdline.my.fctriPadmini 2+:D.npnct01.log`
## `prdline.my.fctriPad 1:D.nstopwrds.log`
## `prdline.my.fctriPad 2:D.nstopwrds.log`
## `prdline.my.fctriPad 3+:D.nstopwrds.log`
## `prdline.my.fctriPadAir:D.nstopwrds.log`
## `prdline.my.fctriPadmini:D.nstopwrds.log`
## `prdline.my.fctriPadmini 2+:D.nstopwrds.log`
## `prdline.my.fctriPad 1:D.npnct08.log`
## `prdline.my.fctriPad 2:D.npnct08.log`
## `prdline.my.fctriPad 3+:D.npnct08.log`
## `prdline.my.fctriPadAir:D.npnct08.log`
## `prdline.my.fctriPadmini:D.npnct08.log`
## `prdline.my.fctriPadmini 2+:D.npnct08.log`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:D.terms.n.post.stem`
## `prdline.my.fctriPad 2:D.terms.n.post.stem`
## `prdline.my.fctriPad 3+:D.terms.n.post.stem`
## `prdline.my.fctriPadAir:D.terms.n.post.stem`
## `prdline.my.fctriPadmini:D.terms.n.post.stem`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stem`
## `prdline.my.fctriPad 1:biddable` *
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable` **
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:color.fctrGold`
## `prdline.my.fctriPad 2:color.fctrGold`
## `prdline.my.fctriPad 3+:color.fctrGold`
## `prdline.my.fctriPadAir:color.fctrGold`
## `prdline.my.fctriPadmini:color.fctrGold`
## `prdline.my.fctriPadmini 2+:color.fctrGold`
## `prdline.my.fctriPad 1:color.fctrSpace Gray`
## `prdline.my.fctriPad 2:color.fctrSpace Gray`
## `prdline.my.fctriPad 3+:color.fctrSpace Gray`
## `prdline.my.fctriPadAir:color.fctrSpace Gray`
## `prdline.my.fctriPadmini:color.fctrSpace Gray`
## `prdline.my.fctriPadmini 2+:color.fctrSpace Gray`
## `prdline.my.fctriPad 1:color.fctrUnknown`
## `prdline.my.fctriPad 2:color.fctrUnknown`
## `prdline.my.fctriPad 3+:color.fctrUnknown`
## `prdline.my.fctriPadAir:color.fctrUnknown`
## `prdline.my.fctriPadmini:color.fctrUnknown`
## `prdline.my.fctriPadmini 2+:color.fctrUnknown`
## `prdline.my.fctriPad 1:color.fctrWhite`
## `prdline.my.fctriPad 2:color.fctrWhite`
## `prdline.my.fctriPad 3+:color.fctrWhite`
## `prdline.my.fctriPadAir:color.fctrWhite`
## `prdline.my.fctriPadmini:color.fctrWhite`
## `prdline.my.fctriPadmini 2+:color.fctrWhite`
## `prdline.my.fctriPad 1:storage.fctr16`
## `prdline.my.fctriPad 2:storage.fctr16`
## `prdline.my.fctriPad 3+:storage.fctr16`
## `prdline.my.fctriPadAir:storage.fctr16`
## `prdline.my.fctriPadmini:storage.fctr16`
## `prdline.my.fctriPadmini 2+:storage.fctr16`
## `prdline.my.fctriPad 1:storage.fctr32`
## `prdline.my.fctriPad 2:storage.fctr32`
## `prdline.my.fctriPad 3+:storage.fctr32`
## `prdline.my.fctriPadAir:storage.fctr32`
## `prdline.my.fctriPadmini:storage.fctr32`
## `prdline.my.fctriPadmini 2+:storage.fctr32`
## `prdline.my.fctriPad 1:storage.fctr64`
## `prdline.my.fctriPad 2:storage.fctr64`
## `prdline.my.fctriPad 3+:storage.fctr64`
## `prdline.my.fctriPadAir:storage.fctr64`
## `prdline.my.fctriPadmini:storage.fctr64`
## `prdline.my.fctriPadmini 2+:storage.fctr64`
## `prdline.my.fctriPad 1:storage.fctrUnknown`
## `prdline.my.fctriPad 2:storage.fctrUnknown`
## `prdline.my.fctriPad 3+:storage.fctrUnknown`
## `prdline.my.fctriPadAir:storage.fctrUnknown` *
## `prdline.my.fctriPadmini:storage.fctrUnknown`
## `prdline.my.fctriPadmini 2+:storage.fctrUnknown`
## `prdline.my.fctriPad 1:idseq.my`
## `prdline.my.fctriPad 2:idseq.my`
## `prdline.my.fctriPad 3+:idseq.my`
## `prdline.my.fctriPadAir:idseq.my`
## `prdline.my.fctriPadmini:idseq.my`
## `prdline.my.fctriPadmini 2+:idseq.my` .
## `cellular.fctr1:carrier.fctrNone`
## `cellular.fctrUnknown:carrier.fctrNone`
## `cellular.fctr1:carrier.fctrOther`
## `cellular.fctrUnknown:carrier.fctrOther`
## `cellular.fctr1:carrier.fctrSprint`
## `cellular.fctrUnknown:carrier.fctrSprint`
## `cellular.fctr1:carrier.fctrT-Mobile`
## `cellular.fctrUnknown:carrier.fctrT-Mobile`
## `cellular.fctr1:carrier.fctrUnknown`
## `cellular.fctrUnknown:carrier.fctrUnknown`
## `cellular.fctr1:carrier.fctrVerizon`
## `cellular.fctrUnknown:carrier.fctrVerizon`
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6939.154)
##
## Null deviance: 14768530 on 859 degrees of freedom
## Residual deviance: 4413302 on 636 degrees of freedom
## AIC: 10238
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 5.331 2.122
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.7011685 102.734 0.5498673 142.8658 10237.73
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.45214 5.574502 0.04977095
## label step_major step_minor bgn end elapsed
## 10 fit.models_1_bayesglm 10 0 182.859 189.725 6.866
## 11 fit.models_1_glmnet 11 0 189.725 NA NA
## [1] "fitting model: All.Interact.X.glmnet"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 12.6 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 22300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 223 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.341415e+02
## prdline.my.fctriPad 1
## -2.866242e+01
## prdline.my.fctriPad 2
## -9.872612e+00
## prdline.my.fctriPadAir
## 5.007070e+01
## prdline.my.fctriPadmini
## -3.442453e+00
## prdline.my.fctriPadmini 2+
## 2.498766e+01
## D.terms.n.stem.stop.Ratio
## -1.771557e+01
## D.npnct05.log
## -4.790069e+01
## D.npnct15.log
## -4.768675e+00
## D.npnct12.log
## 3.615552e-01
## D.npnct11.log
## -5.361100e+00
## D.npnct13.log
## -4.545348e+00
## D.ratio.sum.TfIdf.nwrds
## -1.364845e+01
## D.TfIdf.sum.stem.stop.Ratio
## 1.171722e+02
## D.npnct16.log
## 1.522779e+01
## D.nstopwrds.log
## 3.142634e+00
## D.terms.n.post.stop
## 2.586400e-01
## D.terms.n.post.stem
## 1.902413e-01
## biddable
## -1.129021e+02
## condition.fctrFor parts or not working
## -3.578538e+01
## condition.fctrNew
## 5.004426e+01
## condition.fctrNew other (see details)
## 2.128146e+01
## condition.fctrSeller refurbished
## -1.077182e+01
## color.fctrGold
## 2.596653e+00
## color.fctrSpace Gray
## 1.929174e+01
## color.fctrUnknown
## -1.561687e+00
## color.fctrWhite
## 1.428446e+01
## storage.fctr16
## -3.036063e+01
## storage.fctr32
## -1.688383e+01
## idseq.my
## -6.359974e-03
## cellular.fctr1
## 3.567485e+00
## cellular.fctrUnknown
## -1.309807e+01
## carrier.fctrOther
## 1.313346e+01
## carrier.fctrSprint
## -1.435408e+01
## carrier.fctrT-Mobile
## 1.014140e+01
## carrier.fctrVerizon
## 3.358034e+00
## prdline.my.fctriPadAir:D.nchrs.log
## -3.767773e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -2.812440e+01
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio
## -1.136365e+01
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 5.599476e+01
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## -7.326328e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 2.595432e+01
## prdline.my.fctriPad 1:D.npnct16.log
## 2.651203e+01
## prdline.my.fctriPad 3+:D.npnct16.log
## -1.829399e+01
## prdline.my.fctriPadAir:D.npnct16.log
## 2.751290e+01
## prdline.my.fctriPadmini:D.npnct16.log
## -1.190870e+01
## prdline.my.fctriPadmini 2+:D.npnct16.log
## -6.486176e+01
## prdline.my.fctriPad 2:D.npnct01.log
## 8.494068e+00
## prdline.my.fctriPad 3+:D.npnct01.log
## -1.951682e+01
## prdline.my.fctriPadAir:D.npnct01.log
## 6.345732e+01
## prdline.my.fctriPadmini:D.npnct01.log
## -3.841996e+00
## prdline.my.fctriPadmini 2+:D.npnct01.log
## 8.380753e+01
## prdline.my.fctriPadmini:D.nstopwrds.log
## -1.920209e+00
## prdline.my.fctriPadmini 2+:D.nstopwrds.log
## 5.542161e+00
## prdline.my.fctriPadAir:D.npnct08.log
## 2.674683e+01
## prdline.my.fctriPad 1:biddable
## 6.459227e+00
## prdline.my.fctriPadAir:biddable
## -3.847810e+01
## prdline.my.fctriPadmini 2+:biddable
## -1.707146e+01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 6.546617e+00
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -2.081081e+01
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -6.716751e+01
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -8.027583e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -2.780807e+01
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -1.219675e+01
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -1.182659e+01
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 3.834903e+01
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -1.337271e+02
## prdline.my.fctriPadAir:condition.fctrNew
## 2.771645e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 2.799303e+01
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -2.379591e+01
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -8.230515e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 2.819786e+01
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 2.613940e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 6.497073e+01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## -1.219474e+00
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -9.922925e+01
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 4.768463e+01
## prdline.my.fctriPadAir:color.fctrGold
## 1.975491e+01
## prdline.my.fctriPad 1:color.fctrSpace Gray
## 4.436116e+00
## prdline.my.fctriPad 3+:color.fctrSpace Gray
## 7.110516e+01
## prdline.my.fctriPadAir:color.fctrSpace Gray
## 7.142823e+00
## prdline.my.fctriPadmini:color.fctrSpace Gray
## -1.001494e+01
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray
## 6.248215e+00
## prdline.my.fctriPad 1:color.fctrUnknown
## -2.529375e+00
## prdline.my.fctriPad 2:color.fctrUnknown
## -2.063069e+01
## prdline.my.fctriPad 3+:color.fctrUnknown
## -9.027177e+00
## prdline.my.fctriPadAir:color.fctrUnknown
## 3.072256e+01
## prdline.my.fctriPad 1:color.fctrWhite
## -1.408518e+01
## prdline.my.fctriPad 2:color.fctrWhite
## -1.148246e+01
## prdline.my.fctriPadAir:color.fctrWhite
## 3.561542e+01
## prdline.my.fctriPadmini:color.fctrWhite
## 4.371208e+00
## prdline.my.fctriPadmini 2+:color.fctrWhite
## 1.523585e+01
## prdline.my.fctriPad 1:storage.fctr16
## -4.495476e+00
## prdline.my.fctriPadAir:storage.fctr16
## -1.990231e+01
## prdline.my.fctriPadmini:storage.fctr16
## -5.336713e+00
## prdline.my.fctriPadmini 2+:storage.fctr16
## -2.968190e+01
## prdline.my.fctriPad 1:storage.fctr32
## -3.025015e+00
## prdline.my.fctriPadAir:storage.fctr32
## -2.950383e+01
## prdline.my.fctriPad 1:storage.fctr64
## -1.438114e+01
## prdline.my.fctriPad 2:storage.fctr64
## -2.605981e+00
## prdline.my.fctriPad 3+:storage.fctr64
## 2.769474e+00
## prdline.my.fctriPadAir:storage.fctr64
## 2.599579e+01
## prdline.my.fctriPadmini 2+:storage.fctr64
## 7.900283e+00
## prdline.my.fctriPad 1:storage.fctrUnknown
## 4.943570e+01
## prdline.my.fctriPad 2:storage.fctrUnknown
## -1.776364e+01
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1.345875e+01
## prdline.my.fctriPadAir:storage.fctrUnknown
## -1.560882e+02
## prdline.my.fctriPadmini 2+:storage.fctrUnknown
## 9.630272e+01
## prdline.my.fctriPad 1:idseq.my
## -1.099033e-02
## cellular.fctr1:carrier.fctrOther
## 1.309115e+01
## cellular.fctr1:carrier.fctrSprint
## -1.401091e+01
## cellular.fctr1:carrier.fctrT-Mobile
## 9.998125e+00
## cellular.fctr1:carrier.fctrUnknown
## 1.264845e+01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.297476e+01
## cellular.fctr1:carrier.fctrVerizon
## 3.320665e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## 2.827247e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.464753e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.142896e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -2.467432e+01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 6.459083e+01
## prdline.my.fctriPad 1
## -1.826548e+01
## prdline.my.fctriPad 2
## 1.490248e+01
## prdline.my.fctriPad 3+
## -6.087879e+00
## prdline.my.fctriPadAir
## 1.055822e+02
## prdline.my.fctriPadmini
## 1.430770e+02
## prdline.my.fctriPadmini 2+
## 1.372894e+02
## D.ratio.nstopwrds.nwrds
## -7.173516e+01
## D.npnct14.log
## -3.133446e+01
## D.terms.n.stem.stop.Ratio
## -4.106323e+01
## D.ndgts.log
## 1.984687e+01
## .rnorm
## 1.555316e+00
## D.npnct05.log
## -1.141508e+02
## D.npnct15.log
## -3.883332e+00
## D.npnct12.log
## 2.287467e+00
## D.npnct06.log
## -8.142214e+01
## D.npnct03.log
## -4.413053e+00
## D.npnct11.log
## -2.338982e+01
## D.npnct13.log
## -1.432949e+01
## D.nwrds.log
## 5.638118e+01
## D.terms.n.post.stop.log
## -2.550582e+00
## D.nwrds.unq.log
## -1.908149e-01
## D.terms.n.post.stem.log
## -1.288758e-02
## D.nuppr.log
## -1.589238e-03
## D.npnct24.log
## -9.653552e+01
## D.TfIdf.sum.post.stem
## 1.032163e+00
## D.sum.TfIdf
## 1.003698e+00
## D.ratio.sum.TfIdf.nwrds
## -1.151983e+01
## D.nchrs.log
## -1.054978e+01
## D.TfIdf.sum.stem.stop.Ratio
## 2.383943e+02
## D.npnct16.log
## 1.411424e+02
## D.npnct01.log
## 6.697712e+01
## D.nstopwrds.log
## -3.144361e-01
## D.npnct08.log
## 2.821481e+01
## D.terms.n.post.stop
## 2.088528e-01
## D.terms.n.post.stem
## 5.746694e+00
## biddable
## -1.276399e+02
## condition.fctrFor parts or not working
## -5.797759e+01
## condition.fctrManufacturer refurbished
## 5.270526e+01
## condition.fctrNew
## 5.957182e+01
## condition.fctrNew other (see details)
## -7.850379e+00
## condition.fctrSeller refurbished
## -3.352385e+01
## color.fctrGold
## 1.895357e+00
## color.fctrSpace Gray
## 6.727717e+01
## color.fctrUnknown
## 9.205262e+00
## color.fctrWhite
## 5.460066e+01
## storage.fctr16
## -3.459308e+00
## storage.fctr32
## -1.220467e+02
## storage.fctr64
## -7.045077e+00
## storage.fctrUnknown
## -1.499295e+01
## idseq.my
## 1.177893e-02
## cellular.fctr1
## 5.416722e+00
## cellular.fctrUnknown
## -1.452975e+01
## carrier.fctrOther
## 3.065637e+01
## carrier.fctrSprint
## -2.555713e+01
## carrier.fctrT-Mobile
## 1.506460e+01
## carrier.fctrVerizon
## 5.578859e+00
## prdline.my.fctriPad 1:D.nchrs.log
## -5.075934e+00
## prdline.my.fctriPad 2:D.nchrs.log
## -3.759206e-01
## prdline.my.fctriPad 3+:D.nchrs.log
## 3.094596e+00
## prdline.my.fctriPadAir:D.nchrs.log
## -4.729470e+01
## prdline.my.fctriPadmini:D.nchrs.log
## -1.221881e+01
## prdline.my.fctriPadmini 2+:D.nchrs.log
## -6.083948e-01
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -1.241664e+01
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio
## -4.039650e+01
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio
## 2.887072e+01
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 2.149084e+02
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## -1.958481e+02
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 1.229397e+02
## prdline.my.fctriPad 1:D.npnct16.log
## -3.779760e+01
## prdline.my.fctriPad 2:D.npnct16.log
## -6.959694e+01
## prdline.my.fctriPad 3+:D.npnct16.log
## -1.551199e+02
## prdline.my.fctriPadAir:D.npnct16.log
## -1.210649e+01
## prdline.my.fctriPadmini:D.npnct16.log
## -1.019935e+02
## prdline.my.fctriPadmini 2+:D.npnct16.log
## -1.686235e+02
## prdline.my.fctriPad 1:D.npnct01.log
## -1.014302e+02
## prdline.my.fctriPad 2:D.npnct01.log
## -5.607153e+01
## prdline.my.fctriPad 3+:D.npnct01.log
## -1.481400e+02
## prdline.my.fctriPadAir:D.npnct01.log
## 1.119724e+02
## prdline.my.fctriPadmini:D.npnct01.log
## -8.520820e+01
## prdline.my.fctriPadmini 2+:D.npnct01.log
## -3.153935e+00
## prdline.my.fctriPad 1:D.nstopwrds.log
## -2.519479e+01
## prdline.my.fctriPad 2:D.nstopwrds.log
## -1.092842e+01
## prdline.my.fctriPad 3+:D.nstopwrds.log
## -1.710616e-01
## prdline.my.fctriPadAir:D.nstopwrds.log
## 2.173519e+01
## prdline.my.fctriPadmini:D.nstopwrds.log
## -1.794900e+01
## prdline.my.fctriPadmini 2+:D.nstopwrds.log
## 1.224827e+00
## prdline.my.fctriPad 1:D.npnct08.log
## -4.120280e+01
## prdline.my.fctriPad 2:D.npnct08.log
## -4.807950e+01
## prdline.my.fctriPad 3+:D.npnct08.log
## -1.008700e+01
## prdline.my.fctriPadAir:D.npnct08.log
## 7.353082e+01
## prdline.my.fctriPadmini 2+:D.npnct08.log
## -6.687954e+00
## prdline.my.fctriPad 1:D.terms.n.post.stop
## -5.360868e+00
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -1.376249e+01
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -1.369310e+01
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 3.401036e+00
## prdline.my.fctriPadmini:D.terms.n.post.stop
## -1.464594e+01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## 3.671665e+01
## prdline.my.fctriPad 1:D.terms.n.post.stem
## 1.729938e+00
## prdline.my.fctriPad 2:D.terms.n.post.stem
## 4.367911e+00
## prdline.my.fctriPad 3+:D.terms.n.post.stem
## 1.144075e-01
## prdline.my.fctriPadmini:D.terms.n.post.stem
## 1.134051e+01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem
## -5.045706e+01
## prdline.my.fctriPad 1:biddable
## 7.577341e+01
## prdline.my.fctriPad 2:biddable
## 2.960648e+01
## prdline.my.fctriPad 3+:biddable
## -1.042616e+01
## prdline.my.fctriPadAir:biddable
## -9.151632e+01
## prdline.my.fctriPadmini:biddable
## 2.580313e+01
## prdline.my.fctriPadmini 2+:biddable
## -5.439303e+01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 4.250160e+01
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -8.361886e+00
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -8.244801e+00
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## 1.694185e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## 4.648842e+01
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -1.165744e+02
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished
## -7.636187e+01
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -9.656938e+01
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## -1.264518e+02
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 3.534024e+01
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -2.022275e+02
## prdline.my.fctriPad 1:condition.fctrNew
## 3.150136e+01
## prdline.my.fctriPad 3+:condition.fctrNew
## -3.272537e+01
## prdline.my.fctriPadAir:condition.fctrNew
## 1.532894e+00
## prdline.my.fctriPadmini:condition.fctrNew
## -5.733674e+00
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 1.687965e+01
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -4.169635e+01
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -2.443102e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 3.903570e+01
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 7.189839e+01
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## 4.955460e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 1.611290e+02
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## 1.793130e+01
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## 1.601689e+01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## 9.620224e-01
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -7.626286e+01
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 8.298161e+01
## prdline.my.fctriPad 3+:color.fctrGold
## 2.045627e+00
## prdline.my.fctriPadAir:color.fctrGold
## 4.728805e+01
## prdline.my.fctriPadmini 2+:color.fctrGold
## -7.498437e+00
## prdline.my.fctriPad 1:color.fctrSpace Gray
## 9.919205e+00
## prdline.my.fctriPad 3+:color.fctrSpace Gray
## 4.050163e+01
## prdline.my.fctriPadAir:color.fctrSpace Gray
## -4.542955e+00
## prdline.my.fctriPadmini:color.fctrSpace Gray
## -4.231156e+01
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray
## -4.678595e+01
## prdline.my.fctriPad 1:color.fctrUnknown
## -5.340025e+00
## prdline.my.fctriPad 2:color.fctrUnknown
## -4.705340e+01
## prdline.my.fctriPad 3+:color.fctrUnknown
## -3.934225e+01
## prdline.my.fctriPadAir:color.fctrUnknown
## 5.273685e+01
## prdline.my.fctriPadmini:color.fctrUnknown
## 1.374232e+01
## prdline.my.fctriPadmini 2+:color.fctrUnknown
## -1.329169e+01
## prdline.my.fctriPad 1:color.fctrWhite
## -6.370850e+01
## prdline.my.fctriPad 2:color.fctrWhite
## -6.307949e+01
## prdline.my.fctriPad 3+:color.fctrWhite
## -4.033425e+01
## prdline.my.fctriPadAir:color.fctrWhite
## 1.828653e+01
## prdline.my.fctriPadmini:color.fctrWhite
## -8.280942e+00
## prdline.my.fctriPadmini 2+:color.fctrWhite
## -1.639463e+01
## prdline.my.fctriPad 1:storage.fctr16
## -7.241021e+01
## prdline.my.fctriPad 2:storage.fctr16
## 1.009056e+01
## prdline.my.fctriPad 3+:storage.fctr16
## 2.319292e+01
## prdline.my.fctriPadAir:storage.fctr16
## -1.677078e+02
## prdline.my.fctriPadmini:storage.fctr16
## 2.028858e+01
## prdline.my.fctriPadmini 2+:storage.fctr16
## -1.465943e+02
## prdline.my.fctriPad 1:storage.fctr32
## 4.956722e+01
## prdline.my.fctriPad 2:storage.fctr32
## 1.395068e+02
## prdline.my.fctriPad 3+:storage.fctr32
## 1.568728e+02
## prdline.my.fctriPadAir:storage.fctr32
## -4.067896e+01
## prdline.my.fctriPadmini:storage.fctr32
## 1.459483e+02
## prdline.my.fctriPadmini 2+:storage.fctr32
## 3.872604e+01
## prdline.my.fctriPad 1:storage.fctr64
## -5.359813e+01
## prdline.my.fctriPad 2:storage.fctr64
## 2.835069e+01
## prdline.my.fctriPad 3+:storage.fctr64
## 6.391436e+01
## prdline.my.fctriPadAir:storage.fctr64
## -7.447831e+01
## prdline.my.fctriPadmini:storage.fctr64
## 6.460646e+01
## prdline.my.fctriPadmini 2+:storage.fctr64
## -5.569389e+01
## prdline.my.fctriPad 1:storage.fctrUnknown
## -6.403603e+00
## prdline.my.fctriPad 2:storage.fctrUnknown
## 4.936149e+00
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1.015349e+02
## prdline.my.fctriPadAir:storage.fctrUnknown
## -4.942535e+02
## prdline.my.fctriPadmini:storage.fctrUnknown
## 7.615582e+01
## prdline.my.fctriPadmini 2+:storage.fctrUnknown
## 6.713962e+01
## prdline.my.fctriPad 1:idseq.my
## -1.031963e-02
## prdline.my.fctriPad 2:idseq.my
## -1.622738e-02
## prdline.my.fctriPad 3+:idseq.my
## -2.213384e-02
## prdline.my.fctriPadAir:idseq.my
## -5.199304e-02
## prdline.my.fctriPadmini:idseq.my
## -1.535967e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -7.858674e-02
## cellular.fctr1:carrier.fctrOther
## 2.930204e+01
## cellular.fctr1:carrier.fctrSprint
## -2.201958e+01
## cellular.fctr1:carrier.fctrT-Mobile
## 1.590609e+01
## cellular.fctr1:carrier.fctrUnknown
## 2.152306e+01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.375466e+01
## cellular.fctr1:carrier.fctrVerizon
## 5.579526e+00
## prdline.my.fctrUnknown:.clusterid.fctr2
## -1.052135e+02
## prdline.my.fctriPad 1:.clusterid.fctr2
## -1.004480e+01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 4.950932e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -4.397366e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -9.800875e+00
## prdline.my.fctriPadmini:.clusterid.fctr2
## -5.800037e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.661556e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -3.727195e+01
## prdline.my.fctriPadAir:.clusterid.fctr3
## -7.253117e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.glmnet glmnet
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 3.225 0.216
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.632461 89.67939 0.5880457 136.6729 0.534788
## min.RMSESD.fit max.RsquaredSD.fit
## 1 4.905317 0.05616002
## label step_major step_minor bgn end elapsed
## 11 fit.models_1_glmnet 11 0 189.725 194.919 5.194
## 12 fit.models_1_rpart 12 0 194.919 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rpart"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0895 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 860
##
## CP nsplit rel error
## 1 0.22941102 0 1.0000000
## 2 0.10081265 1 0.7705890
## 3 0.08946164 2 0.6697763
##
## Variable importance
## biddable
## 38
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 17
## prdline.my.fctriPadAir
## 10
## prdline.my.fctriPadAir:biddable
## 10
## prdline.my.fctriPadAir:idseq.my
## 10
## prdline.my.fctriPadAir:condition.fctrNew
## 5
## prdline.my.fctriPadAir:storage.fctr64
## 4
## idseq.my
## 2
## prdline.my.fctriPadmini 2+:idseq.my
## 2
## D.npnct15.log
## 1
## prdline.my.fctriPad 1:idseq.my
## 1
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1
##
## Node number 1: 860 observations, complexity param=0.229411
## mean=127.4371, MSE=17172.71
## left son=2 (640 obs) right son=3 (220 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.2294110, (0 missing)
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio < 0.9860857 to the left, improve=0.1926094, (0 missing)
## prdline.my.fctriPadAir:idseq.my < 9.5 to the left, improve=0.1478139, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.1478139, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.1303927, (0 missing)
## Surrogate splits:
## idseq.my < 1783.5 to the left, agree=0.757, adj=0.050, (0 split)
## prdline.my.fctriPadmini 2+:idseq.my < 1420.5 to the left, agree=0.756, adj=0.045, (0 split)
## D.npnct15.log < 0.3465736 to the left, agree=0.750, adj=0.023, (0 split)
## prdline.my.fctriPad 3+:storage.fctrUnknown < 0.5 to the left, agree=0.750, adj=0.023, (0 split)
## prdline.my.fctriPad 1:idseq.my < 1666.5 to the left, agree=0.750, adj=0.023, (0 split)
##
## Node number 2: 640 observations, complexity param=0.1008127
## mean=90.63711, MSE=11139.65
## left son=4 (560 obs) right son=5 (80 obs)
## Primary splits:
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio < 0.9860857 to the left, improve=0.2088338, (0 missing)
## prdline.my.fctriPadAir:idseq.my < 42 to the left, improve=0.1689339, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.1664350, (0 missing)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, improve=0.1664350, (0 missing)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, improve=0.1100772, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir < 0.5 to the left, agree=0.948, adj=0.588, (0 split)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, agree=0.948, adj=0.588, (0 split)
## prdline.my.fctriPadAir:idseq.my < 17 to the left, agree=0.948, adj=0.588, (0 split)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, agree=0.914, adj=0.312, (0 split)
## prdline.my.fctriPadAir:storage.fctr64 < 0.5 to the left, agree=0.903, adj=0.225, (0 split)
##
## Node number 3: 220 observations
## mean=234.4917, MSE=19323.14
##
## Node number 4: 560 observations
## mean=72.40709, MSE=6033.669
##
## Node number 5: 80 observations
## mean=218.2473, MSE=28270.82
##
## n= 860
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 860 14768530 127.43710
## 2) biddable>=0.5 640 7129375 90.63711
## 4) prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio< 0.9860857 560 3378855 72.40709 *
## 5) prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio>=0.9860857 80 2261666 218.24730 *
## 3) biddable< 0.5 220 4251091 234.49170 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.no.rnorm.rpart rpart
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 2.13 0.135
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.3302237 106.4364 0.3385299 173.186 0.3447065
## min.RMSESD.fit max.RsquaredSD.fit
## 1 7.737579 0.0889796
## label step_major step_minor bgn end elapsed
## 12 fit.models_1_rpart 12 0 194.919 199.454 4.535
## 13 fit.models_1_rf 13 0 199.455 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rf"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 112 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 860 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 860 -none- numeric
## importance 222 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 860 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 222 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.no.rnorm.rf rf
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 59.718 20.829
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.8974081 92.5966 0.6065945 133.4265 0.510817
## min.RMSESD.fit max.RsquaredSD.fit
## 1 4.688399 0.04474049# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# model_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# #,"prdline.my.fctr*idseq.my"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.n.post.stop"
# #,"prdline.my.fctr*D.terms.n.post.stem"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glb_models_method_vctr) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## All.Interact.X.lm All.Interact.X.lm lm
## All.Interact.X.glm All.Interact.X.glm glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.cv.0.cp.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.rpart biddable, prdline.my.fctr
## Max.cor.Y.lm biddable, prdline.my.fctr
## Interact.High.cor.Y.lm biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem
## Low.cor.X.lm prdline.my.fctr, condition.fctr, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.lm prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glm prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.lm prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.480
## Max.cor.Y.cv.0.rpart 0 0.618
## Max.cor.Y.cv.0.cp.0.rpart 0 0.490
## Max.cor.Y.rpart 3 1.005
## Max.cor.Y.lm 1 0.938
## Interact.High.cor.Y.lm 1 0.982
## Low.cor.X.lm 1 1.033
## All.X.lm 1 1.035
## All.X.glm 1 1.102
## All.X.bayesglm 1 2.428
## All.X.glmnet 9 1.676
## All.X.no.rnorm.rpart 3 1.377
## All.X.no.rnorm.rf 3 22.449
## All.Interact.X.lm 1 1.400
## All.Interact.X.glm 1 1.489
## All.Interact.X.bayesglm 1 5.331
## All.Interact.X.glmnet 9 3.225
## All.Interact.X.no.rnorm.rpart 3 2.130
## All.Interact.X.no.rnorm.rf 3 59.718
## min.elapsedtime.final max.R.sq.fit
## MFO.lm 0.003 7.226357e-05
## Max.cor.Y.cv.0.rpart 0.011 0.000000e+00
## Max.cor.Y.cv.0.cp.0.rpart 0.011 4.923724e-01
## Max.cor.Y.rpart 0.011 3.121279e-01
## Max.cor.Y.lm 0.006 4.594170e-01
## Interact.High.cor.Y.lm 0.009 4.736677e-01
## Low.cor.X.lm 0.027 5.956499e-01
## All.X.lm 0.033 6.065530e-01
## All.X.glm 0.047 6.065530e-01
## All.X.bayesglm 0.344 6.056393e-01
## All.X.glmnet 0.054 5.761356e-01
## All.X.no.rnorm.rpart 0.053 3.121279e-01
## All.X.no.rnorm.rf 7.239 8.913483e-01
## All.Interact.X.lm 0.132 7.035928e-01
## All.Interact.X.glm 0.199 7.035928e-01
## All.Interact.X.bayesglm 2.122 7.011685e-01
## All.Interact.X.glmnet 0.216 6.324610e-01
## All.Interact.X.no.rnorm.rpart 0.135 3.302237e-01
## All.Interact.X.no.rnorm.rf 20.829 8.974081e-01
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## MFO.lm 131.03995 0.0001316983 212.9262
## Max.cor.Y.cv.0.rpart 131.04468 0.0000000000 212.9402
## Max.cor.Y.cv.0.cp.0.rpart 93.36670 0.5489639195 143.0090
## Max.cor.Y.rpart 111.83847 0.4505450166 157.8425
## Max.cor.Y.lm 97.12892 0.5186350614 147.7389
## Interact.High.cor.Y.lm 96.61314 0.5213161959 147.3269
## Low.cor.X.lm 91.52464 0.6157209924 132.0022
## All.X.lm 95.96146 0.5925190808 135.9288
## All.X.glm 95.96146 0.5925190808 135.9288
## All.X.bayesglm 93.87814 0.5971058616 135.1617
## All.X.glmnet 89.92115 0.5750593534 138.8104
## All.X.no.rnorm.rpart 111.83847 0.4505450166 157.8425
## All.X.no.rnorm.rf 91.20519 0.6232976153 130.5761
## All.Interact.X.lm 109.52912 0.5299790909 145.9877
## All.Interact.X.glm 109.52912 0.5299790909 145.9877
## All.Interact.X.bayesglm 102.73398 0.5498673432 142.8658
## All.Interact.X.glmnet 89.67939 0.5880457216 136.6729
## All.Interact.X.no.rnorm.rpart 106.43636 0.3385298763 173.1860
## All.Interact.X.no.rnorm.rf 92.59660 0.6065945051 133.4265
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.001093153 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.2750573
## Max.cor.Y.lm 0.454975555 0.4524550
## Interact.High.cor.Y.lm 0.463678006 0.4581469
## Low.cor.X.lm 0.569595158 0.5176304
## All.X.lm 0.574343836 0.4835691
## All.X.glm NA 0.4835691
## All.X.bayesglm NA 0.4991002
## All.X.glmnet NA 0.5328404
## All.X.no.rnorm.rpart NA 0.2750573
## All.X.no.rnorm.rf NA 0.5247944
## All.Interact.X.lm 0.618841627 0.4185045
## All.Interact.X.glm NA 0.4185045
## All.Interact.X.bayesglm NA 0.4521400
## All.Interact.X.glmnet NA 0.5347880
## All.Interact.X.no.rnorm.rpart NA 0.3447065
## All.Interact.X.no.rnorm.rf NA 0.5108170
## min.RMSESD.fit max.RsquaredSD.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 3.592112 0.04148092
## Max.cor.Y.lm 3.422758 0.04130826
## Interact.High.cor.Y.lm 3.790992 0.04312606
## Low.cor.X.lm 1.527531 0.01733469
## All.X.lm 4.088978 0.03215851
## All.X.glm 4.088978 0.03215851
## All.X.bayesglm 2.970354 0.02413754
## All.X.glmnet 3.551696 0.04348867
## All.X.no.rnorm.rpart 3.592112 0.04148092
## All.X.no.rnorm.rf 5.596350 0.05641606
## All.Interact.X.lm 3.436445 0.04064351
## All.Interact.X.glm 3.436445 0.04064351
## All.Interact.X.bayesglm 5.574502 0.04977095
## All.Interact.X.glmnet 4.905317 0.05616002
## All.Interact.X.no.rnorm.rpart 7.737579 0.08897960
## All.Interact.X.no.rnorm.rf 4.688399 0.04474049
## min.aic.fit
## MFO.lm NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.lm NA
## Interact.High.cor.Y.lm NA
## Low.cor.X.lm NA
## All.X.lm NA
## All.X.glm 10158.28
## All.X.bayesglm 10176.28
## All.X.glmnet NA
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NA
## All.Interact.X.lm NA
## All.Interact.X.glm 10166.72
## All.Interact.X.bayesglm 10237.73
## All.Interact.X.glmnet NA
## All.Interact.X.no.rnorm.rpart NA
## All.Interact.X.no.rnorm.rf NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models_1_rf 13 0 199.455 261.591 62.136
## 14 fit.models_1_end 14 0 261.592 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 132.159 261.598 129.439
## 12 fit.models 7 2 261.598 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## All.Interact.X.lm All.Interact.X.lm lm
## All.Interact.X.glm All.Interact.X.glm glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.cv.0.cp.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.rpart biddable, prdline.my.fctr
## Max.cor.Y.lm biddable, prdline.my.fctr
## Interact.High.cor.Y.lm biddable, prdline.my.fctr, biddable:D.TfIdf.sum.post.stop, biddable:D.npnct06.log, biddable:D.npnct03.log, biddable:D.terms.n.post.stem, biddable:D.nuppr.log, biddable:D.nwrds.unq.log, biddable:D.npnct24.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.TfIdf.sum.post.stem
## Low.cor.X.lm prdline.my.fctr, condition.fctr, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct12.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.lm prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glm prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf prdline.my.fctr, condition.fctr, D.ratio.nstopwrds.nwrds, D.TfIdf.sum.stem.stop.Ratio, color.fctr, carrier.fctr, storage.fctr, D.npnct14.log, cellular.fctr, D.terms.n.stem.stop.Ratio, D.ndgts.log, idseq.my, D.npnct08.log, D.npnct05.log, D.npnct15.log, D.npnct01.log, D.npnct16.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.nstopwrds.log, D.npnct11.log, D.npnct13.log, D.terms.n.post.stop, D.terms.n.post.stem, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, biddable, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.lm prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB
## MFO.lm 0 7.226357e-05 0.0001316983
## Max.cor.Y.cv.0.rpart 0 0.000000e+00 0.0000000000
## Max.cor.Y.cv.0.cp.0.rpart 0 4.923724e-01 0.5489639195
## Max.cor.Y.rpart 3 3.121279e-01 0.4505450166
## Max.cor.Y.lm 1 4.594170e-01 0.5186350614
## Interact.High.cor.Y.lm 1 4.736677e-01 0.5213161959
## Low.cor.X.lm 1 5.956499e-01 0.6157209924
## All.X.lm 1 6.065530e-01 0.5925190808
## All.X.glm 1 6.065530e-01 0.5925190808
## All.X.bayesglm 1 6.056393e-01 0.5971058616
## All.X.glmnet 9 5.761356e-01 0.5750593534
## All.X.no.rnorm.rpart 3 3.121279e-01 0.4505450166
## All.X.no.rnorm.rf 3 8.913483e-01 0.6232976153
## All.Interact.X.lm 1 7.035928e-01 0.5299790909
## All.Interact.X.glm 1 7.035928e-01 0.5299790909
## All.Interact.X.bayesglm 1 7.011685e-01 0.5498673432
## All.Interact.X.glmnet 9 6.324610e-01 0.5880457216
## All.Interact.X.no.rnorm.rpart 3 3.302237e-01 0.3385298763
## All.Interact.X.no.rnorm.rf 3 8.974081e-01 0.6065945051
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.001093153 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.2750573
## Max.cor.Y.lm 0.454975555 0.4524550
## Interact.High.cor.Y.lm 0.463678006 0.4581469
## Low.cor.X.lm 0.569595158 0.5176304
## All.X.lm 0.574343836 0.4835691
## All.X.glm NA 0.4835691
## All.X.bayesglm NA 0.4991002
## All.X.glmnet NA 0.5328404
## All.X.no.rnorm.rpart NA 0.2750573
## All.X.no.rnorm.rf NA 0.5247944
## All.Interact.X.lm 0.618841627 0.4185045
## All.Interact.X.glm NA 0.4185045
## All.Interact.X.bayesglm NA 0.4521400
## All.Interact.X.glmnet NA 0.5347880
## All.Interact.X.no.rnorm.rpart NA 0.3447065
## All.Interact.X.no.rnorm.rf NA 0.5108170
## inv.elapsedtime.everything
## MFO.lm 2.08333333
## Max.cor.Y.cv.0.rpart 1.61812298
## Max.cor.Y.cv.0.cp.0.rpart 2.04081633
## Max.cor.Y.rpart 0.99502488
## Max.cor.Y.lm 1.06609808
## Interact.High.cor.Y.lm 1.01832994
## Low.cor.X.lm 0.96805421
## All.X.lm 0.96618357
## All.X.glm 0.90744102
## All.X.bayesglm 0.41186161
## All.X.glmnet 0.59665871
## All.X.no.rnorm.rpart 0.72621641
## All.X.no.rnorm.rf 0.04454541
## All.Interact.X.lm 0.71428571
## All.Interact.X.glm 0.67159167
## All.Interact.X.bayesglm 0.18758207
## All.Interact.X.glmnet 0.31007752
## All.Interact.X.no.rnorm.rpart 0.46948357
## All.Interact.X.no.rnorm.rf 0.01674537
## inv.elapsedtime.final inv.RMSE.fit
## MFO.lm 333.33333333 0.007631261
## Max.cor.Y.cv.0.rpart 90.90909091 0.007630985
## Max.cor.Y.cv.0.cp.0.rpart 90.90909091 0.010710456
## Max.cor.Y.rpart 90.90909091 0.008941467
## Max.cor.Y.lm 166.66666667 0.010295595
## Interact.High.cor.Y.lm 111.11111111 0.010350559
## Low.cor.X.lm 37.03703704 0.010926019
## All.X.lm 30.30303030 0.010420850
## All.X.glm 21.27659574 0.010420850
## All.X.bayesglm 2.90697674 0.010652107
## All.X.glmnet 18.51851852 0.011120854
## All.X.no.rnorm.rpart 18.86792453 0.008941467
## All.X.no.rnorm.rf 0.13814063 0.010964288
## All.Interact.X.lm 7.57575758 0.009129992
## All.Interact.X.glm 5.02512563 0.009129992
## All.Interact.X.bayesglm 0.47125353 0.009733877
## All.Interact.X.glmnet 4.62962963 0.011150834
## All.Interact.X.no.rnorm.rpart 7.40740741 0.009395285
## All.Interact.X.no.rnorm.rf 0.04800999 0.010799533
## inv.RMSE.OOB inv.aic.fit
## MFO.lm 0.004696462 NA
## Max.cor.Y.cv.0.rpart 0.004696153 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.006992566 NA
## Max.cor.Y.rpart 0.006335431 NA
## Max.cor.Y.lm 0.006768696 NA
## Interact.High.cor.Y.lm 0.006787626 NA
## Low.cor.X.lm 0.007575633 NA
## All.X.lm 0.007356794 NA
## All.X.glm 0.007356794 9.844182e-05
## All.X.bayesglm 0.007398547 9.826774e-05
## All.X.glmnet 0.007204073 NA
## All.X.no.rnorm.rpart 0.006335431 NA
## All.X.no.rnorm.rf 0.007658371 NA
## All.Interact.X.lm 0.006849891 NA
## All.Interact.X.glm 0.006849891 9.836012e-05
## All.Interact.X.bayesglm 0.006999575 9.767793e-05
## All.Interact.X.glmnet 0.007316742 NA
## All.Interact.X.no.rnorm.rpart 0.005774138 NA
## All.Interact.X.no.rnorm.rf 0.007494764 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 19. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 141 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 19. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 3 rows containing missing values (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 3 rows containing missing values (position_stack).
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id min.RMSE.fit max.R.sq.fit
## 17 All.Interact.X.glmnet 89.67939 6.324610e-01
## 11 All.X.glmnet 89.92115 5.761356e-01
## 13 All.X.no.rnorm.rf 91.20519 8.913483e-01
## 7 Low.cor.X.lm 91.52464 5.956499e-01
## 19 All.Interact.X.no.rnorm.rf 92.59660 8.974081e-01
## 3 Max.cor.Y.cv.0.cp.0.rpart 93.36670 4.923724e-01
## 10 All.X.bayesglm 93.87814 6.056393e-01
## 8 All.X.lm 95.96146 6.065530e-01
## 9 All.X.glm 95.96146 6.065530e-01
## 6 Interact.High.cor.Y.lm 96.61314 4.736677e-01
## 5 Max.cor.Y.lm 97.12892 4.594170e-01
## 16 All.Interact.X.bayesglm 102.73398 7.011685e-01
## 18 All.Interact.X.no.rnorm.rpart 106.43636 3.302237e-01
## 15 All.Interact.X.glm 109.52912 7.035928e-01
## 14 All.Interact.X.lm 109.52912 7.035928e-01
## 12 All.X.no.rnorm.rpart 111.83847 3.121279e-01
## 4 Max.cor.Y.rpart 111.83847 3.121279e-01
## 1 MFO.lm 131.03995 7.226357e-05
## 2 Max.cor.Y.cv.0.rpart 131.04468 0.000000e+00
## max.Adj.R.sq.fit
## 17 NA
## 11 NA
## 13 NA
## 7 0.569595158
## 19 NA
## 3 NA
## 10 NA
## 8 0.574343836
## 9 NA
## 6 0.463678006
## 5 0.454975555
## 16 NA
## 18 NA
## 15 NA
## 14 0.618841627
## 12 NA
## 4 NA
## 1 -0.001093153
## 2 NAprint(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 19. Consider specifying shapes manually if you must have them.## Warning: Removed 8 rows containing missing values (geom_path).## Warning: Removed 49 rows containing missing values (geom_point).## Warning: Removed 13 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 19. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.fit - max.R.sq.fit - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.Interact.X.glmnet"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 100 -none- numeric
## beta 22300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 223 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.341415e+02
## prdline.my.fctriPad 1
## -2.866242e+01
## prdline.my.fctriPad 2
## -9.872612e+00
## prdline.my.fctriPadAir
## 5.007070e+01
## prdline.my.fctriPadmini
## -3.442453e+00
## prdline.my.fctriPadmini 2+
## 2.498766e+01
## D.terms.n.stem.stop.Ratio
## -1.771557e+01
## D.npnct05.log
## -4.790069e+01
## D.npnct15.log
## -4.768675e+00
## D.npnct12.log
## 3.615552e-01
## D.npnct11.log
## -5.361100e+00
## D.npnct13.log
## -4.545348e+00
## D.ratio.sum.TfIdf.nwrds
## -1.364845e+01
## D.TfIdf.sum.stem.stop.Ratio
## 1.171722e+02
## D.npnct16.log
## 1.522779e+01
## D.nstopwrds.log
## 3.142634e+00
## D.terms.n.post.stop
## 2.586400e-01
## D.terms.n.post.stem
## 1.902413e-01
## biddable
## -1.129021e+02
## condition.fctrFor parts or not working
## -3.578538e+01
## condition.fctrNew
## 5.004426e+01
## condition.fctrNew other (see details)
## 2.128146e+01
## condition.fctrSeller refurbished
## -1.077182e+01
## color.fctrGold
## 2.596653e+00
## color.fctrSpace Gray
## 1.929174e+01
## color.fctrUnknown
## -1.561687e+00
## color.fctrWhite
## 1.428446e+01
## storage.fctr16
## -3.036063e+01
## storage.fctr32
## -1.688383e+01
## idseq.my
## -6.359974e-03
## cellular.fctr1
## 3.567485e+00
## cellular.fctrUnknown
## -1.309807e+01
## carrier.fctrOther
## 1.313346e+01
## carrier.fctrSprint
## -1.435408e+01
## carrier.fctrT-Mobile
## 1.014140e+01
## carrier.fctrVerizon
## 3.358034e+00
## prdline.my.fctriPadAir:D.nchrs.log
## -3.767773e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -2.812440e+01
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio
## -1.136365e+01
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 5.599476e+01
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## -7.326328e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 2.595432e+01
## prdline.my.fctriPad 1:D.npnct16.log
## 2.651203e+01
## prdline.my.fctriPad 3+:D.npnct16.log
## -1.829399e+01
## prdline.my.fctriPadAir:D.npnct16.log
## 2.751290e+01
## prdline.my.fctriPadmini:D.npnct16.log
## -1.190870e+01
## prdline.my.fctriPadmini 2+:D.npnct16.log
## -6.486176e+01
## prdline.my.fctriPad 2:D.npnct01.log
## 8.494068e+00
## prdline.my.fctriPad 3+:D.npnct01.log
## -1.951682e+01
## prdline.my.fctriPadAir:D.npnct01.log
## 6.345732e+01
## prdline.my.fctriPadmini:D.npnct01.log
## -3.841996e+00
## prdline.my.fctriPadmini 2+:D.npnct01.log
## 8.380753e+01
## prdline.my.fctriPadmini:D.nstopwrds.log
## -1.920209e+00
## prdline.my.fctriPadmini 2+:D.nstopwrds.log
## 5.542161e+00
## prdline.my.fctriPadAir:D.npnct08.log
## 2.674683e+01
## prdline.my.fctriPad 1:biddable
## 6.459227e+00
## prdline.my.fctriPadAir:biddable
## -3.847810e+01
## prdline.my.fctriPadmini 2+:biddable
## -1.707146e+01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 6.546617e+00
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -2.081081e+01
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -6.716751e+01
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -8.027583e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -2.780807e+01
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -1.219675e+01
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -1.182659e+01
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 3.834903e+01
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -1.337271e+02
## prdline.my.fctriPadAir:condition.fctrNew
## 2.771645e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 2.799303e+01
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -2.379591e+01
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -8.230515e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 2.819786e+01
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 2.613940e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 6.497073e+01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## -1.219474e+00
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -9.922925e+01
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 4.768463e+01
## prdline.my.fctriPadAir:color.fctrGold
## 1.975491e+01
## prdline.my.fctriPad 1:color.fctrSpace Gray
## 4.436116e+00
## prdline.my.fctriPad 3+:color.fctrSpace Gray
## 7.110516e+01
## prdline.my.fctriPadAir:color.fctrSpace Gray
## 7.142823e+00
## prdline.my.fctriPadmini:color.fctrSpace Gray
## -1.001494e+01
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray
## 6.248215e+00
## prdline.my.fctriPad 1:color.fctrUnknown
## -2.529375e+00
## prdline.my.fctriPad 2:color.fctrUnknown
## -2.063069e+01
## prdline.my.fctriPad 3+:color.fctrUnknown
## -9.027177e+00
## prdline.my.fctriPadAir:color.fctrUnknown
## 3.072256e+01
## prdline.my.fctriPad 1:color.fctrWhite
## -1.408518e+01
## prdline.my.fctriPad 2:color.fctrWhite
## -1.148246e+01
## prdline.my.fctriPadAir:color.fctrWhite
## 3.561542e+01
## prdline.my.fctriPadmini:color.fctrWhite
## 4.371208e+00
## prdline.my.fctriPadmini 2+:color.fctrWhite
## 1.523585e+01
## prdline.my.fctriPad 1:storage.fctr16
## -4.495476e+00
## prdline.my.fctriPadAir:storage.fctr16
## -1.990231e+01
## prdline.my.fctriPadmini:storage.fctr16
## -5.336713e+00
## prdline.my.fctriPadmini 2+:storage.fctr16
## -2.968190e+01
## prdline.my.fctriPad 1:storage.fctr32
## -3.025015e+00
## prdline.my.fctriPadAir:storage.fctr32
## -2.950383e+01
## prdline.my.fctriPad 1:storage.fctr64
## -1.438114e+01
## prdline.my.fctriPad 2:storage.fctr64
## -2.605981e+00
## prdline.my.fctriPad 3+:storage.fctr64
## 2.769474e+00
## prdline.my.fctriPadAir:storage.fctr64
## 2.599579e+01
## prdline.my.fctriPadmini 2+:storage.fctr64
## 7.900283e+00
## prdline.my.fctriPad 1:storage.fctrUnknown
## 4.943570e+01
## prdline.my.fctriPad 2:storage.fctrUnknown
## -1.776364e+01
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1.345875e+01
## prdline.my.fctriPadAir:storage.fctrUnknown
## -1.560882e+02
## prdline.my.fctriPadmini 2+:storage.fctrUnknown
## 9.630272e+01
## prdline.my.fctriPad 1:idseq.my
## -1.099033e-02
## cellular.fctr1:carrier.fctrOther
## 1.309115e+01
## cellular.fctr1:carrier.fctrSprint
## -1.401091e+01
## cellular.fctr1:carrier.fctrT-Mobile
## 9.998125e+00
## cellular.fctr1:carrier.fctrUnknown
## 1.264845e+01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.297476e+01
## cellular.fctr1:carrier.fctrVerizon
## 3.320665e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## 2.827247e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.464753e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.142896e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -2.467432e+01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 6.459083e+01
## prdline.my.fctriPad 1
## -1.826548e+01
## prdline.my.fctriPad 2
## 1.490248e+01
## prdline.my.fctriPad 3+
## -6.087879e+00
## prdline.my.fctriPadAir
## 1.055822e+02
## prdline.my.fctriPadmini
## 1.430770e+02
## prdline.my.fctriPadmini 2+
## 1.372894e+02
## D.ratio.nstopwrds.nwrds
## -7.173516e+01
## D.npnct14.log
## -3.133446e+01
## D.terms.n.stem.stop.Ratio
## -4.106323e+01
## D.ndgts.log
## 1.984687e+01
## .rnorm
## 1.555316e+00
## D.npnct05.log
## -1.141508e+02
## D.npnct15.log
## -3.883332e+00
## D.npnct12.log
## 2.287467e+00
## D.npnct06.log
## -8.142214e+01
## D.npnct03.log
## -4.413053e+00
## D.npnct11.log
## -2.338982e+01
## D.npnct13.log
## -1.432949e+01
## D.nwrds.log
## 5.638118e+01
## D.terms.n.post.stop.log
## -2.550582e+00
## D.nwrds.unq.log
## -1.908149e-01
## D.terms.n.post.stem.log
## -1.288758e-02
## D.nuppr.log
## -1.589238e-03
## D.npnct24.log
## -9.653552e+01
## D.TfIdf.sum.post.stem
## 1.032163e+00
## D.sum.TfIdf
## 1.003698e+00
## D.ratio.sum.TfIdf.nwrds
## -1.151983e+01
## D.nchrs.log
## -1.054978e+01
## D.TfIdf.sum.stem.stop.Ratio
## 2.383943e+02
## D.npnct16.log
## 1.411424e+02
## D.npnct01.log
## 6.697712e+01
## D.nstopwrds.log
## -3.144361e-01
## D.npnct08.log
## 2.821481e+01
## D.terms.n.post.stop
## 2.088528e-01
## D.terms.n.post.stem
## 5.746694e+00
## biddable
## -1.276399e+02
## condition.fctrFor parts or not working
## -5.797759e+01
## condition.fctrManufacturer refurbished
## 5.270526e+01
## condition.fctrNew
## 5.957182e+01
## condition.fctrNew other (see details)
## -7.850379e+00
## condition.fctrSeller refurbished
## -3.352385e+01
## color.fctrGold
## 1.895357e+00
## color.fctrSpace Gray
## 6.727717e+01
## color.fctrUnknown
## 9.205262e+00
## color.fctrWhite
## 5.460066e+01
## storage.fctr16
## -3.459308e+00
## storage.fctr32
## -1.220467e+02
## storage.fctr64
## -7.045077e+00
## storage.fctrUnknown
## -1.499295e+01
## idseq.my
## 1.177893e-02
## cellular.fctr1
## 5.416722e+00
## cellular.fctrUnknown
## -1.452975e+01
## carrier.fctrOther
## 3.065637e+01
## carrier.fctrSprint
## -2.555713e+01
## carrier.fctrT-Mobile
## 1.506460e+01
## carrier.fctrVerizon
## 5.578859e+00
## prdline.my.fctriPad 1:D.nchrs.log
## -5.075934e+00
## prdline.my.fctriPad 2:D.nchrs.log
## -3.759206e-01
## prdline.my.fctriPad 3+:D.nchrs.log
## 3.094596e+00
## prdline.my.fctriPadAir:D.nchrs.log
## -4.729470e+01
## prdline.my.fctriPadmini:D.nchrs.log
## -1.221881e+01
## prdline.my.fctriPadmini 2+:D.nchrs.log
## -6.083948e-01
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -1.241664e+01
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio
## -4.039650e+01
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio
## 2.887072e+01
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 2.149084e+02
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## -1.958481e+02
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 1.229397e+02
## prdline.my.fctriPad 1:D.npnct16.log
## -3.779760e+01
## prdline.my.fctriPad 2:D.npnct16.log
## -6.959694e+01
## prdline.my.fctriPad 3+:D.npnct16.log
## -1.551199e+02
## prdline.my.fctriPadAir:D.npnct16.log
## -1.210649e+01
## prdline.my.fctriPadmini:D.npnct16.log
## -1.019935e+02
## prdline.my.fctriPadmini 2+:D.npnct16.log
## -1.686235e+02
## prdline.my.fctriPad 1:D.npnct01.log
## -1.014302e+02
## prdline.my.fctriPad 2:D.npnct01.log
## -5.607153e+01
## prdline.my.fctriPad 3+:D.npnct01.log
## -1.481400e+02
## prdline.my.fctriPadAir:D.npnct01.log
## 1.119724e+02
## prdline.my.fctriPadmini:D.npnct01.log
## -8.520820e+01
## prdline.my.fctriPadmini 2+:D.npnct01.log
## -3.153935e+00
## prdline.my.fctriPad 1:D.nstopwrds.log
## -2.519479e+01
## prdline.my.fctriPad 2:D.nstopwrds.log
## -1.092842e+01
## prdline.my.fctriPad 3+:D.nstopwrds.log
## -1.710616e-01
## prdline.my.fctriPadAir:D.nstopwrds.log
## 2.173519e+01
## prdline.my.fctriPadmini:D.nstopwrds.log
## -1.794900e+01
## prdline.my.fctriPadmini 2+:D.nstopwrds.log
## 1.224827e+00
## prdline.my.fctriPad 1:D.npnct08.log
## -4.120280e+01
## prdline.my.fctriPad 2:D.npnct08.log
## -4.807950e+01
## prdline.my.fctriPad 3+:D.npnct08.log
## -1.008700e+01
## prdline.my.fctriPadAir:D.npnct08.log
## 7.353082e+01
## prdline.my.fctriPadmini 2+:D.npnct08.log
## -6.687954e+00
## prdline.my.fctriPad 1:D.terms.n.post.stop
## -5.360868e+00
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -1.376249e+01
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -1.369310e+01
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 3.401036e+00
## prdline.my.fctriPadmini:D.terms.n.post.stop
## -1.464594e+01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## 3.671665e+01
## prdline.my.fctriPad 1:D.terms.n.post.stem
## 1.729938e+00
## prdline.my.fctriPad 2:D.terms.n.post.stem
## 4.367911e+00
## prdline.my.fctriPad 3+:D.terms.n.post.stem
## 1.144075e-01
## prdline.my.fctriPadmini:D.terms.n.post.stem
## 1.134051e+01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem
## -5.045706e+01
## prdline.my.fctriPad 1:biddable
## 7.577341e+01
## prdline.my.fctriPad 2:biddable
## 2.960648e+01
## prdline.my.fctriPad 3+:biddable
## -1.042616e+01
## prdline.my.fctriPadAir:biddable
## -9.151632e+01
## prdline.my.fctriPadmini:biddable
## 2.580313e+01
## prdline.my.fctriPadmini 2+:biddable
## -5.439303e+01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 4.250160e+01
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -8.361886e+00
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -8.244801e+00
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## 1.694185e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## 4.648842e+01
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -1.165744e+02
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished
## -7.636187e+01
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -9.656938e+01
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## -1.264518e+02
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 3.534024e+01
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -2.022275e+02
## prdline.my.fctriPad 1:condition.fctrNew
## 3.150136e+01
## prdline.my.fctriPad 3+:condition.fctrNew
## -3.272537e+01
## prdline.my.fctriPadAir:condition.fctrNew
## 1.532894e+00
## prdline.my.fctriPadmini:condition.fctrNew
## -5.733674e+00
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 1.687965e+01
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -4.169635e+01
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -2.443102e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 3.903570e+01
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 7.189839e+01
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## 4.955460e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 1.611290e+02
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## 1.793130e+01
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## 1.601689e+01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## 9.620224e-01
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -7.626286e+01
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 8.298161e+01
## prdline.my.fctriPad 3+:color.fctrGold
## 2.045627e+00
## prdline.my.fctriPadAir:color.fctrGold
## 4.728805e+01
## prdline.my.fctriPadmini 2+:color.fctrGold
## -7.498437e+00
## prdline.my.fctriPad 1:color.fctrSpace Gray
## 9.919205e+00
## prdline.my.fctriPad 3+:color.fctrSpace Gray
## 4.050163e+01
## prdline.my.fctriPadAir:color.fctrSpace Gray
## -4.542955e+00
## prdline.my.fctriPadmini:color.fctrSpace Gray
## -4.231156e+01
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray
## -4.678595e+01
## prdline.my.fctriPad 1:color.fctrUnknown
## -5.340025e+00
## prdline.my.fctriPad 2:color.fctrUnknown
## -4.705340e+01
## prdline.my.fctriPad 3+:color.fctrUnknown
## -3.934225e+01
## prdline.my.fctriPadAir:color.fctrUnknown
## 5.273685e+01
## prdline.my.fctriPadmini:color.fctrUnknown
## 1.374232e+01
## prdline.my.fctriPadmini 2+:color.fctrUnknown
## -1.329169e+01
## prdline.my.fctriPad 1:color.fctrWhite
## -6.370850e+01
## prdline.my.fctriPad 2:color.fctrWhite
## -6.307949e+01
## prdline.my.fctriPad 3+:color.fctrWhite
## -4.033425e+01
## prdline.my.fctriPadAir:color.fctrWhite
## 1.828653e+01
## prdline.my.fctriPadmini:color.fctrWhite
## -8.280942e+00
## prdline.my.fctriPadmini 2+:color.fctrWhite
## -1.639463e+01
## prdline.my.fctriPad 1:storage.fctr16
## -7.241021e+01
## prdline.my.fctriPad 2:storage.fctr16
## 1.009056e+01
## prdline.my.fctriPad 3+:storage.fctr16
## 2.319292e+01
## prdline.my.fctriPadAir:storage.fctr16
## -1.677078e+02
## prdline.my.fctriPadmini:storage.fctr16
## 2.028858e+01
## prdline.my.fctriPadmini 2+:storage.fctr16
## -1.465943e+02
## prdline.my.fctriPad 1:storage.fctr32
## 4.956722e+01
## prdline.my.fctriPad 2:storage.fctr32
## 1.395068e+02
## prdline.my.fctriPad 3+:storage.fctr32
## 1.568728e+02
## prdline.my.fctriPadAir:storage.fctr32
## -4.067896e+01
## prdline.my.fctriPadmini:storage.fctr32
## 1.459483e+02
## prdline.my.fctriPadmini 2+:storage.fctr32
## 3.872604e+01
## prdline.my.fctriPad 1:storage.fctr64
## -5.359813e+01
## prdline.my.fctriPad 2:storage.fctr64
## 2.835069e+01
## prdline.my.fctriPad 3+:storage.fctr64
## 6.391436e+01
## prdline.my.fctriPadAir:storage.fctr64
## -7.447831e+01
## prdline.my.fctriPadmini:storage.fctr64
## 6.460646e+01
## prdline.my.fctriPadmini 2+:storage.fctr64
## -5.569389e+01
## prdline.my.fctriPad 1:storage.fctrUnknown
## -6.403603e+00
## prdline.my.fctriPad 2:storage.fctrUnknown
## 4.936149e+00
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1.015349e+02
## prdline.my.fctriPadAir:storage.fctrUnknown
## -4.942535e+02
## prdline.my.fctriPadmini:storage.fctrUnknown
## 7.615582e+01
## prdline.my.fctriPadmini 2+:storage.fctrUnknown
## 6.713962e+01
## prdline.my.fctriPad 1:idseq.my
## -1.031963e-02
## prdline.my.fctriPad 2:idseq.my
## -1.622738e-02
## prdline.my.fctriPad 3+:idseq.my
## -2.213384e-02
## prdline.my.fctriPadAir:idseq.my
## -5.199304e-02
## prdline.my.fctriPadmini:idseq.my
## -1.535967e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -7.858674e-02
## cellular.fctr1:carrier.fctrOther
## 2.930204e+01
## cellular.fctr1:carrier.fctrSprint
## -2.201958e+01
## cellular.fctr1:carrier.fctrT-Mobile
## 1.590609e+01
## cellular.fctr1:carrier.fctrUnknown
## 2.152306e+01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.375466e+01
## cellular.fctr1:carrier.fctrVerizon
## 5.579526e+00
## prdline.my.fctrUnknown:.clusterid.fctr2
## -1.052135e+02
## prdline.my.fctriPad 1:.clusterid.fctr2
## -1.004480e+01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 4.950932e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -4.397366e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -9.800875e+00
## prdline.my.fctriPadmini:.clusterid.fctr2
## -5.800037e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.661556e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -3.727195e+01
## prdline.my.fctriPadAir:.clusterid.fctr3
## -7.253117e+01
## character(0)
## character(0)## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 1282 11283
## 2632 12634
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 1282 0 948.98 New 1 Unknown
## 2632 0 700.00 Used 1 Verizon
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 1282 Gold 128 iPad mini 3 Test <NA> -0.3303767 1283
## 2632 Unknown 32 iPad 2 Test <NA> 0.8127608 2634
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 1282 iPadmini 2+ 6.855388
## 2632 iPad 2 6.551080
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 1282 New 1 Unknown Gold
## 2632 Used 1 Verizon Unknown
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 7
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 1282 128 iPadmini 2+ 0
## 2632 32 iPad 2 7
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.079442 8.846628 7
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 1282 0.000000 0.000000 0
## 2632 2.079442 6.429203 7
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.079442 7.656131
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 1282 0.000000 0.000000
## 2632 2.079442 6.340152
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.cosmet
## 2623 1 0.8654292 0
## 1396 1 1.0000000 0
## 1418 1 1.0000000 0
## 2501 1 1.0000000 0
## 1282 1 1.0000000 0
## 2632 1 0.9861490 0
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 2623 2.944439 2.079442 7.656131 0.4253406
## 1396 0.000000 0.000000 0.000000 0.0000000
## 1418 0.000000 0.000000 0.000000 0.0000000
## 2501 0.000000 0.000000 0.000000 0.0000000
## 1282 0.000000 0.000000 0.000000 0.0000000
## 2632 2.484907 2.079442 6.340152 0.5763775
## D.nchrs.log D.nuppr.log D.ndgts.log D.npnct01.log D.npnct03.log
## 2623 4.634729 4.356709 1.098612 0 0
## 1396 0.000000 0.000000 0.000000 0 0
## 1418 0.000000 0.000000 0.000000 0 0
## 2501 0.000000 0.000000 0.000000 0 0
## 1282 0.000000 0.000000 0.000000 0 0
## 2632 4.077537 3.713572 1.609438 0 0
## D.npnct05.log D.npnct06.log D.npnct08.log D.npnct11.log D.npnct12.log
## 2623 0 0.6931472 0 0.6931472 0
## 1396 0 0.0000000 0 0.0000000 0
## 1418 0 0.0000000 0 0.0000000 0
## 2501 0 0.0000000 0 0.0000000 0
## 1282 0 0.0000000 0 0.0000000 0
## 2632 0 0.0000000 0 0.0000000 0
## D.npnct13.log D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log
## 2623 1.098612 0 0 0.6931472 0.6931472
## 1396 0.000000 0 0 0.0000000 0.0000000
## 1418 0.000000 0 0 0.0000000 0.0000000
## 2501 0.000000 0 0 0.0000000 0.0000000
## 1282 0.000000 0 0 0.0000000 0.0000000
## 2632 1.098612 0 0 0.0000000 0.6931472
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black
## 2623 2.302585 0.5263158 1 0 0
## 1396 0.000000 1.0000000 0 0 0
## 1418 0.000000 1.0000000 0 0 0
## 2501 0.000000 1.0000000 0 0 0
## 1282 0.000000 1.0000000 0 0 0
## 2632 1.098612 0.2500000 0 0 0
## D.P.white .clusterid .clusterid.fctr
## 2623 0 2 2
## 1396 0 1 1
## 1418 0 1 1
## 2501 0 1 1
## 1282 0 1 1
## 2632 0 1 1
## startprice.predict.All.Interact.X.glmnet
## 2623 133.68160
## 1396 195.47496
## 1418 83.96559
## 2501 276.73079
## 1282 373.46076
## 2632 158.99268
## startprice.predict.All.Interact.X.glmnet.err
## 2623 866.3084
## 1396 803.5250
## 1418 616.0344
## 2501 603.2592
## 1282 575.5192
## 2632 541.0073predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
predct_error_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".err")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## prdline.my.fctriPad 3+ 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.npnct14.log 57.154652
## D.ndgts.log 57.154652
## .rnorm 57.154652
## D.npnct06.log 57.154652
## D.npnct03.log 57.154652
## D.nwrds.log 57.154652
## D.terms.n.post.stop.log 57.154652
## D.nwrds.unq.log 57.154652
## D.terms.n.post.stem.log 57.154652
## D.nuppr.log 57.154652
## D.npnct24.log 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.sum.TfIdf 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.npnct01.log 57.154652
## D.npnct08.log 57.154652
## condition.fctrManufacturer refurbished 57.154652
## storage.fctr64 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000
## All.Interact.X.glmnet.importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## prdline.my.fctriPad 3+ 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.npnct14.log 57.154652
## D.ndgts.log 57.154652
## .rnorm 57.154652
## D.npnct06.log 57.154652
## D.npnct03.log 57.154652
## D.nwrds.log 57.154652
## D.terms.n.post.stop.log 57.154652
## D.nwrds.unq.log 57.154652
## D.terms.n.post.stem.log 57.154652
## D.nuppr.log 57.154652
## D.npnct24.log 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.sum.TfIdf 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.npnct01.log 57.154652
## D.npnct08.log 57.154652
## condition.fctrManufacturer refurbished 57.154652
## storage.fctr64 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 53
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 1282 11283
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 1282 0 948.98 New 1 Unknown
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 1282 Gold 128 iPad mini 3 Test <NA> -0.3303767 1283
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 1282 iPadmini 2+ 6.855388
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 1282 New 1 Unknown Gold
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 7
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 1282 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.079442 8.846628 7
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 1282 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.079442 7.656131
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 1282 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.cosmet
## 2623 1 0.8654292 0
## 1396 1 1.0000000 0
## 1418 1 1.0000000 0
## 2501 1 1.0000000 0
## 1282 1 1.0000000 0
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 2623 2.944439 2.079442 7.656131 0.4253406
## 1396 0.000000 0.000000 0.000000 0.0000000
## 1418 0.000000 0.000000 0.000000 0.0000000
## 2501 0.000000 0.000000 0.000000 0.0000000
## 1282 0.000000 0.000000 0.000000 0.0000000
## D.nchrs.log D.nuppr.log D.ndgts.log D.npnct01.log D.npnct03.log
## 2623 4.634729 4.356709 1.098612 0 0
## 1396 0.000000 0.000000 0.000000 0 0
## 1418 0.000000 0.000000 0.000000 0 0
## 2501 0.000000 0.000000 0.000000 0 0
## 1282 0.000000 0.000000 0.000000 0 0
## D.npnct05.log D.npnct06.log D.npnct08.log D.npnct11.log D.npnct12.log
## 2623 0 0.6931472 0 0.6931472 0
## 1396 0 0.0000000 0 0.0000000 0
## 1418 0 0.0000000 0 0.0000000 0
## 2501 0 0.0000000 0 0.0000000 0
## 1282 0 0.0000000 0 0.0000000 0
## D.npnct13.log D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log
## 2623 1.098612 0 0 0.6931472 0.6931472
## 1396 0.000000 0 0 0.0000000 0.0000000
## 1418 0.000000 0 0 0.0000000 0.0000000
## 2501 0.000000 0 0 0.0000000 0.0000000
## 1282 0.000000 0 0 0.0000000 0.0000000
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black
## 2623 2.302585 0.5263158 1 0 0
## 1396 0.000000 1.0000000 0 0 0
## 1418 0.000000 1.0000000 0 0 0
## 2501 0.000000 1.0000000 0 0 0
## 1282 0.000000 1.0000000 0 0 0
## D.P.white .clusterid .clusterid.fctr
## 2623 0 2 2
## 1396 0 1 1
## 1418 0 1 1
## 2501 0 1 1
## 1282 0 1 1
## startprice.predict.All.Interact.X.glmnet
## 2623 133.68160
## 1396 195.47496
## 1418 83.96559
## 2501 276.73079
## 1282 373.46076
## startprice.predict.All.Interact.X.glmnet.err
## 2623 866.3084
## 1396 803.5250
## 1418 616.0344
## 2501 603.2592
## 1282 575.5192
## startprice.predict.All.Interact.X.glmnet.accurate .label
## 2623 FALSE 12625
## 1396 FALSE 11397
## 1418 FALSE 11419
## 2501 FALSE 12503
## 1282 FALSE 11283
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 261.598 280.59 18.993
## 13 fit.models 7 3 280.591 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "startprice.predict.All.Interact.X.glmnet"
## [2] "startprice.predict.All.Interact.X.glmnet.err"
## [3] "startprice.predict.All.Interact.X.glmnet.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 280.591 287.381 6.79
## 14 fit.data.training 8 0 287.381 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## [1] "fitting model: Final.glmnet"
## [1] " indep_vars: prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 22300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 223 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.341415e+02
## prdline.my.fctriPad 1
## -2.866242e+01
## prdline.my.fctriPad 2
## -9.872612e+00
## prdline.my.fctriPadAir
## 5.007070e+01
## prdline.my.fctriPadmini
## -3.442453e+00
## prdline.my.fctriPadmini 2+
## 2.498766e+01
## D.terms.n.stem.stop.Ratio
## -1.771557e+01
## D.npnct05.log
## -4.790069e+01
## D.npnct15.log
## -4.768675e+00
## D.npnct12.log
## 3.615552e-01
## D.npnct11.log
## -5.361100e+00
## D.npnct13.log
## -4.545348e+00
## D.ratio.sum.TfIdf.nwrds
## -1.364845e+01
## D.TfIdf.sum.stem.stop.Ratio
## 1.171722e+02
## D.npnct16.log
## 1.522779e+01
## D.nstopwrds.log
## 3.142634e+00
## D.terms.n.post.stop
## 2.586400e-01
## D.terms.n.post.stem
## 1.902413e-01
## biddable
## -1.129021e+02
## condition.fctrFor parts or not working
## -3.578538e+01
## condition.fctrNew
## 5.004426e+01
## condition.fctrNew other (see details)
## 2.128146e+01
## condition.fctrSeller refurbished
## -1.077182e+01
## color.fctrGold
## 2.596653e+00
## color.fctrSpace Gray
## 1.929174e+01
## color.fctrUnknown
## -1.561687e+00
## color.fctrWhite
## 1.428446e+01
## storage.fctr16
## -3.036063e+01
## storage.fctr32
## -1.688383e+01
## idseq.my
## -6.359974e-03
## cellular.fctr1
## 3.567485e+00
## cellular.fctrUnknown
## -1.309807e+01
## carrier.fctrOther
## 1.313346e+01
## carrier.fctrSprint
## -1.435408e+01
## carrier.fctrT-Mobile
## 1.014140e+01
## carrier.fctrVerizon
## 3.358034e+00
## prdline.my.fctriPadAir:D.nchrs.log
## -3.767773e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -2.812440e+01
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio
## -1.136365e+01
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 5.599476e+01
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## -7.326328e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 2.595432e+01
## prdline.my.fctriPad 1:D.npnct16.log
## 2.651203e+01
## prdline.my.fctriPad 3+:D.npnct16.log
## -1.829399e+01
## prdline.my.fctriPadAir:D.npnct16.log
## 2.751290e+01
## prdline.my.fctriPadmini:D.npnct16.log
## -1.190870e+01
## prdline.my.fctriPadmini 2+:D.npnct16.log
## -6.486176e+01
## prdline.my.fctriPad 2:D.npnct01.log
## 8.494068e+00
## prdline.my.fctriPad 3+:D.npnct01.log
## -1.951682e+01
## prdline.my.fctriPadAir:D.npnct01.log
## 6.345732e+01
## prdline.my.fctriPadmini:D.npnct01.log
## -3.841996e+00
## prdline.my.fctriPadmini 2+:D.npnct01.log
## 8.380753e+01
## prdline.my.fctriPadmini:D.nstopwrds.log
## -1.920209e+00
## prdline.my.fctriPadmini 2+:D.nstopwrds.log
## 5.542161e+00
## prdline.my.fctriPadAir:D.npnct08.log
## 2.674683e+01
## prdline.my.fctriPad 1:biddable
## 6.459227e+00
## prdline.my.fctriPadAir:biddable
## -3.847810e+01
## prdline.my.fctriPadmini 2+:biddable
## -1.707146e+01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 6.546617e+00
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -2.081081e+01
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -6.716751e+01
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -8.027583e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -2.780807e+01
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -1.219675e+01
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -1.182659e+01
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 3.834903e+01
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -1.337271e+02
## prdline.my.fctriPadAir:condition.fctrNew
## 2.771645e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 2.799303e+01
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -2.379591e+01
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -8.230515e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 2.819786e+01
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 2.613940e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 6.497073e+01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## -1.219474e+00
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -9.922925e+01
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 4.768463e+01
## prdline.my.fctriPadAir:color.fctrGold
## 1.975491e+01
## prdline.my.fctriPad 1:color.fctrSpace Gray
## 4.436116e+00
## prdline.my.fctriPad 3+:color.fctrSpace Gray
## 7.110516e+01
## prdline.my.fctriPadAir:color.fctrSpace Gray
## 7.142823e+00
## prdline.my.fctriPadmini:color.fctrSpace Gray
## -1.001494e+01
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray
## 6.248215e+00
## prdline.my.fctriPad 1:color.fctrUnknown
## -2.529375e+00
## prdline.my.fctriPad 2:color.fctrUnknown
## -2.063069e+01
## prdline.my.fctriPad 3+:color.fctrUnknown
## -9.027177e+00
## prdline.my.fctriPadAir:color.fctrUnknown
## 3.072256e+01
## prdline.my.fctriPad 1:color.fctrWhite
## -1.408518e+01
## prdline.my.fctriPad 2:color.fctrWhite
## -1.148246e+01
## prdline.my.fctriPadAir:color.fctrWhite
## 3.561542e+01
## prdline.my.fctriPadmini:color.fctrWhite
## 4.371208e+00
## prdline.my.fctriPadmini 2+:color.fctrWhite
## 1.523585e+01
## prdline.my.fctriPad 1:storage.fctr16
## -4.495476e+00
## prdline.my.fctriPadAir:storage.fctr16
## -1.990231e+01
## prdline.my.fctriPadmini:storage.fctr16
## -5.336713e+00
## prdline.my.fctriPadmini 2+:storage.fctr16
## -2.968190e+01
## prdline.my.fctriPad 1:storage.fctr32
## -3.025015e+00
## prdline.my.fctriPadAir:storage.fctr32
## -2.950383e+01
## prdline.my.fctriPad 1:storage.fctr64
## -1.438114e+01
## prdline.my.fctriPad 2:storage.fctr64
## -2.605981e+00
## prdline.my.fctriPad 3+:storage.fctr64
## 2.769474e+00
## prdline.my.fctriPadAir:storage.fctr64
## 2.599579e+01
## prdline.my.fctriPadmini 2+:storage.fctr64
## 7.900283e+00
## prdline.my.fctriPad 1:storage.fctrUnknown
## 4.943570e+01
## prdline.my.fctriPad 2:storage.fctrUnknown
## -1.776364e+01
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1.345875e+01
## prdline.my.fctriPadAir:storage.fctrUnknown
## -1.560882e+02
## prdline.my.fctriPadmini 2+:storage.fctrUnknown
## 9.630272e+01
## prdline.my.fctriPad 1:idseq.my
## -1.099033e-02
## cellular.fctr1:carrier.fctrOther
## 1.309115e+01
## cellular.fctr1:carrier.fctrSprint
## -1.401091e+01
## cellular.fctr1:carrier.fctrT-Mobile
## 9.998125e+00
## cellular.fctr1:carrier.fctrUnknown
## 1.264845e+01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.297476e+01
## cellular.fctr1:carrier.fctrVerizon
## 3.320665e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## 2.827247e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.464753e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.142896e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -2.467432e+01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 6.459083e+01
## prdline.my.fctriPad 1
## -1.826548e+01
## prdline.my.fctriPad 2
## 1.490248e+01
## prdline.my.fctriPad 3+
## -6.087879e+00
## prdline.my.fctriPadAir
## 1.055822e+02
## prdline.my.fctriPadmini
## 1.430770e+02
## prdline.my.fctriPadmini 2+
## 1.372894e+02
## D.ratio.nstopwrds.nwrds
## -7.173516e+01
## D.npnct14.log
## -3.133446e+01
## D.terms.n.stem.stop.Ratio
## -4.106323e+01
## D.ndgts.log
## 1.984687e+01
## .rnorm
## 1.555316e+00
## D.npnct05.log
## -1.141508e+02
## D.npnct15.log
## -3.883332e+00
## D.npnct12.log
## 2.287467e+00
## D.npnct06.log
## -8.142214e+01
## D.npnct03.log
## -4.413053e+00
## D.npnct11.log
## -2.338982e+01
## D.npnct13.log
## -1.432949e+01
## D.nwrds.log
## 5.638118e+01
## D.terms.n.post.stop.log
## -2.550582e+00
## D.nwrds.unq.log
## -1.908149e-01
## D.terms.n.post.stem.log
## -1.288758e-02
## D.nuppr.log
## -1.589238e-03
## D.npnct24.log
## -9.653552e+01
## D.TfIdf.sum.post.stem
## 1.032163e+00
## D.sum.TfIdf
## 1.003698e+00
## D.ratio.sum.TfIdf.nwrds
## -1.151983e+01
## D.nchrs.log
## -1.054978e+01
## D.TfIdf.sum.stem.stop.Ratio
## 2.383943e+02
## D.npnct16.log
## 1.411424e+02
## D.npnct01.log
## 6.697712e+01
## D.nstopwrds.log
## -3.144361e-01
## D.npnct08.log
## 2.821481e+01
## D.terms.n.post.stop
## 2.088528e-01
## D.terms.n.post.stem
## 5.746694e+00
## biddable
## -1.276399e+02
## condition.fctrFor parts or not working
## -5.797759e+01
## condition.fctrManufacturer refurbished
## 5.270526e+01
## condition.fctrNew
## 5.957182e+01
## condition.fctrNew other (see details)
## -7.850379e+00
## condition.fctrSeller refurbished
## -3.352385e+01
## color.fctrGold
## 1.895357e+00
## color.fctrSpace Gray
## 6.727717e+01
## color.fctrUnknown
## 9.205262e+00
## color.fctrWhite
## 5.460066e+01
## storage.fctr16
## -3.459308e+00
## storage.fctr32
## -1.220467e+02
## storage.fctr64
## -7.045077e+00
## storage.fctrUnknown
## -1.499295e+01
## idseq.my
## 1.177893e-02
## cellular.fctr1
## 5.416722e+00
## cellular.fctrUnknown
## -1.452975e+01
## carrier.fctrOther
## 3.065637e+01
## carrier.fctrSprint
## -2.555713e+01
## carrier.fctrT-Mobile
## 1.506460e+01
## carrier.fctrVerizon
## 5.578859e+00
## prdline.my.fctriPad 1:D.nchrs.log
## -5.075934e+00
## prdline.my.fctriPad 2:D.nchrs.log
## -3.759206e-01
## prdline.my.fctriPad 3+:D.nchrs.log
## 3.094596e+00
## prdline.my.fctriPadAir:D.nchrs.log
## -4.729470e+01
## prdline.my.fctriPadmini:D.nchrs.log
## -1.221881e+01
## prdline.my.fctriPadmini 2+:D.nchrs.log
## -6.083948e-01
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -1.241664e+01
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio
## -4.039650e+01
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio
## 2.887072e+01
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 2.149084e+02
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## -1.958481e+02
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 1.229397e+02
## prdline.my.fctriPad 1:D.npnct16.log
## -3.779760e+01
## prdline.my.fctriPad 2:D.npnct16.log
## -6.959694e+01
## prdline.my.fctriPad 3+:D.npnct16.log
## -1.551199e+02
## prdline.my.fctriPadAir:D.npnct16.log
## -1.210649e+01
## prdline.my.fctriPadmini:D.npnct16.log
## -1.019935e+02
## prdline.my.fctriPadmini 2+:D.npnct16.log
## -1.686235e+02
## prdline.my.fctriPad 1:D.npnct01.log
## -1.014302e+02
## prdline.my.fctriPad 2:D.npnct01.log
## -5.607153e+01
## prdline.my.fctriPad 3+:D.npnct01.log
## -1.481400e+02
## prdline.my.fctriPadAir:D.npnct01.log
## 1.119724e+02
## prdline.my.fctriPadmini:D.npnct01.log
## -8.520820e+01
## prdline.my.fctriPadmini 2+:D.npnct01.log
## -3.153935e+00
## prdline.my.fctriPad 1:D.nstopwrds.log
## -2.519479e+01
## prdline.my.fctriPad 2:D.nstopwrds.log
## -1.092842e+01
## prdline.my.fctriPad 3+:D.nstopwrds.log
## -1.710616e-01
## prdline.my.fctriPadAir:D.nstopwrds.log
## 2.173519e+01
## prdline.my.fctriPadmini:D.nstopwrds.log
## -1.794900e+01
## prdline.my.fctriPadmini 2+:D.nstopwrds.log
## 1.224827e+00
## prdline.my.fctriPad 1:D.npnct08.log
## -4.120280e+01
## prdline.my.fctriPad 2:D.npnct08.log
## -4.807950e+01
## prdline.my.fctriPad 3+:D.npnct08.log
## -1.008700e+01
## prdline.my.fctriPadAir:D.npnct08.log
## 7.353082e+01
## prdline.my.fctriPadmini 2+:D.npnct08.log
## -6.687954e+00
## prdline.my.fctriPad 1:D.terms.n.post.stop
## -5.360868e+00
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -1.376249e+01
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -1.369310e+01
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 3.401036e+00
## prdline.my.fctriPadmini:D.terms.n.post.stop
## -1.464594e+01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## 3.671665e+01
## prdline.my.fctriPad 1:D.terms.n.post.stem
## 1.729938e+00
## prdline.my.fctriPad 2:D.terms.n.post.stem
## 4.367911e+00
## prdline.my.fctriPad 3+:D.terms.n.post.stem
## 1.144075e-01
## prdline.my.fctriPadmini:D.terms.n.post.stem
## 1.134051e+01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem
## -5.045706e+01
## prdline.my.fctriPad 1:biddable
## 7.577341e+01
## prdline.my.fctriPad 2:biddable
## 2.960648e+01
## prdline.my.fctriPad 3+:biddable
## -1.042616e+01
## prdline.my.fctriPadAir:biddable
## -9.151632e+01
## prdline.my.fctriPadmini:biddable
## 2.580313e+01
## prdline.my.fctriPadmini 2+:biddable
## -5.439303e+01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 4.250160e+01
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -8.361886e+00
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -8.244801e+00
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## 1.694185e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## 4.648842e+01
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -1.165744e+02
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished
## -7.636187e+01
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -9.656938e+01
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## -1.264518e+02
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## 3.534024e+01
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -2.022275e+02
## prdline.my.fctriPad 1:condition.fctrNew
## 3.150136e+01
## prdline.my.fctriPad 3+:condition.fctrNew
## -3.272537e+01
## prdline.my.fctriPadAir:condition.fctrNew
## 1.532894e+00
## prdline.my.fctriPadmini:condition.fctrNew
## -5.733674e+00
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 1.687965e+01
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -4.169635e+01
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -2.443102e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 3.903570e+01
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 7.189839e+01
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## 4.955460e+01
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 1.611290e+02
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## 1.793130e+01
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## 1.601689e+01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## 9.620224e-01
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -7.626286e+01
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 8.298161e+01
## prdline.my.fctriPad 3+:color.fctrGold
## 2.045627e+00
## prdline.my.fctriPadAir:color.fctrGold
## 4.728805e+01
## prdline.my.fctriPadmini 2+:color.fctrGold
## -7.498437e+00
## prdline.my.fctriPad 1:color.fctrSpace Gray
## 9.919205e+00
## prdline.my.fctriPad 3+:color.fctrSpace Gray
## 4.050163e+01
## prdline.my.fctriPadAir:color.fctrSpace Gray
## -4.542955e+00
## prdline.my.fctriPadmini:color.fctrSpace Gray
## -4.231156e+01
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray
## -4.678595e+01
## prdline.my.fctriPad 1:color.fctrUnknown
## -5.340025e+00
## prdline.my.fctriPad 2:color.fctrUnknown
## -4.705340e+01
## prdline.my.fctriPad 3+:color.fctrUnknown
## -3.934225e+01
## prdline.my.fctriPadAir:color.fctrUnknown
## 5.273685e+01
## prdline.my.fctriPadmini:color.fctrUnknown
## 1.374232e+01
## prdline.my.fctriPadmini 2+:color.fctrUnknown
## -1.329169e+01
## prdline.my.fctriPad 1:color.fctrWhite
## -6.370850e+01
## prdline.my.fctriPad 2:color.fctrWhite
## -6.307949e+01
## prdline.my.fctriPad 3+:color.fctrWhite
## -4.033425e+01
## prdline.my.fctriPadAir:color.fctrWhite
## 1.828653e+01
## prdline.my.fctriPadmini:color.fctrWhite
## -8.280942e+00
## prdline.my.fctriPadmini 2+:color.fctrWhite
## -1.639463e+01
## prdline.my.fctriPad 1:storage.fctr16
## -7.241021e+01
## prdline.my.fctriPad 2:storage.fctr16
## 1.009056e+01
## prdline.my.fctriPad 3+:storage.fctr16
## 2.319292e+01
## prdline.my.fctriPadAir:storage.fctr16
## -1.677078e+02
## prdline.my.fctriPadmini:storage.fctr16
## 2.028858e+01
## prdline.my.fctriPadmini 2+:storage.fctr16
## -1.465943e+02
## prdline.my.fctriPad 1:storage.fctr32
## 4.956722e+01
## prdline.my.fctriPad 2:storage.fctr32
## 1.395068e+02
## prdline.my.fctriPad 3+:storage.fctr32
## 1.568728e+02
## prdline.my.fctriPadAir:storage.fctr32
## -4.067896e+01
## prdline.my.fctriPadmini:storage.fctr32
## 1.459483e+02
## prdline.my.fctriPadmini 2+:storage.fctr32
## 3.872604e+01
## prdline.my.fctriPad 1:storage.fctr64
## -5.359813e+01
## prdline.my.fctriPad 2:storage.fctr64
## 2.835069e+01
## prdline.my.fctriPad 3+:storage.fctr64
## 6.391436e+01
## prdline.my.fctriPadAir:storage.fctr64
## -7.447831e+01
## prdline.my.fctriPadmini:storage.fctr64
## 6.460646e+01
## prdline.my.fctriPadmini 2+:storage.fctr64
## -5.569389e+01
## prdline.my.fctriPad 1:storage.fctrUnknown
## -6.403603e+00
## prdline.my.fctriPad 2:storage.fctrUnknown
## 4.936149e+00
## prdline.my.fctriPad 3+:storage.fctrUnknown
## 1.015349e+02
## prdline.my.fctriPadAir:storage.fctrUnknown
## -4.942535e+02
## prdline.my.fctriPadmini:storage.fctrUnknown
## 7.615582e+01
## prdline.my.fctriPadmini 2+:storage.fctrUnknown
## 6.713962e+01
## prdline.my.fctriPad 1:idseq.my
## -1.031963e-02
## prdline.my.fctriPad 2:idseq.my
## -1.622738e-02
## prdline.my.fctriPad 3+:idseq.my
## -2.213384e-02
## prdline.my.fctriPadAir:idseq.my
## -5.199304e-02
## prdline.my.fctriPadmini:idseq.my
## -1.535967e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -7.858674e-02
## cellular.fctr1:carrier.fctrOther
## 2.930204e+01
## cellular.fctr1:carrier.fctrSprint
## -2.201958e+01
## cellular.fctr1:carrier.fctrT-Mobile
## 1.590609e+01
## cellular.fctr1:carrier.fctrUnknown
## 2.152306e+01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.375466e+01
## cellular.fctr1:carrier.fctrVerizon
## 5.579526e+00
## prdline.my.fctrUnknown:.clusterid.fctr2
## -1.052135e+02
## prdline.my.fctriPad 1:.clusterid.fctr2
## -1.004480e+01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 4.950932e+01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -4.397366e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -9.800875e+00
## prdline.my.fctriPadmini:.clusterid.fctr2
## -5.800037e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.661556e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -3.727195e+01
## prdline.my.fctriPadAir:.clusterid.fctr3
## -7.253117e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## model_id model_method
## 1 Final.glmnet glmnet
## feats
## 1 prdline.my.fctr, D.ratio.nstopwrds.nwrds, D.npnct14.log, D.terms.n.stem.stop.Ratio, D.ndgts.log, .rnorm, D.npnct05.log, D.npnct15.log, D.npnct12.log, D.npnct06.log, D.npnct03.log, D.npnct11.log, D.npnct13.log, D.nwrds.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.nchrs.log, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct16.log, prdline.my.fctr*D.npnct01.log, prdline.my.fctr*D.nstopwrds.log, prdline.my.fctr*D.npnct08.log, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*D.terms.n.post.stem, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*color.fctr, prdline.my.fctr*storage.fctr, prdline.my.fctr*idseq.my, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.562 0.237
## max.R.sq.fit min.RMSE.fit max.Rsquared.fit min.RMSESD.fit
## 1 0.632461 89.67939 0.534788 4.905317
## max.RsquaredSD.fit
## 1 0.05616002rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 287.381 290.46 3.079
## 15 fit.data.training 8 1 290.461 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## UniqueID
## 1704 11705
## 1299 11300
## 1358 11359
## 792 10792
## 794 10794
## 438 10438
## description
## 1704
## 1299
## 1358
## 792
## 794 Excellent Used Condition. Please see all photos and description.
## 438
## biddable startprice condition cellular carrier color storage
## 1704 0 590 New Unknown Unknown White 16
## 1299 1 650 New 1 Unknown Gold 128
## 1358 0 595 New Unknown Unknown Unknown Unknown
## 792 1 550 Used 0 None Gold 128
## 794 1 525 Used 0 None White 128
## 438 1 1 New 0 None Gold 64
## productline .src .grpid .rnorm idseq.my prdline.my
## 1704 Unknown Train <NA> -0.9839429 1705 Unknown
## 1299 iPad Air 2 Train <NA> 1.3370014 1300 iPadAir
## 1358 Unknown Train <NA> -0.3914823 1359 Unknown
## 792 iPad Air 2 Train <NA> -0.2098595 792 iPadAir
## 794 iPad Air 2 Train <NA> -2.3482830 794 iPadAir
## 438 iPad Air 2 Train <NA> -1.6211072 438 iPadAir
## startprice.log
## 1704 6.380123
## 1299 6.476972
## 1358 6.388561
## 792 6.309918
## 794 6.263398
## 438 0.000000
## descr.my
## 1704
## 1299
## 1358
## 792
## 794 Excellent Used Condition. Please see all photos and description.
## 438
## condition.fctr cellular.fctr carrier.fctr color.fctr storage.fctr
## 1704 New Unknown Unknown White 16
## 1299 New 1 Unknown Gold 128
## 1358 New Unknown Unknown Unknown Unknown
## 792 Used 0 None Gold 128
## 794 Used 0 None White 128
## 438 New 0 None Gold 64
## prdline.my.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1704 Unknown 0 0.000000
## 1299 iPadAir 0 0.000000
## 1358 Unknown 0 0.000000
## 792 iPadAir 0 0.000000
## 794 iPadAir 7 2.079442
## 438 iPadAir 0 0.000000
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1704 0.000000 0 0.000000
## 1299 0.000000 0 0.000000
## 1358 0.000000 0 0.000000
## 792 0.000000 0 0.000000
## 794 4.983944 7 2.079442
## 438 0.000000 0 0.000000
## D.TfIdf.sum.post.stem D.terms.n.stem.stop.Ratio
## 1704 0.000000 1
## 1299 0.000000 1
## 1358 0.000000 1
## 792 0.000000 1
## 794 4.926312 1
## 438 0.000000 1
## D.TfIdf.sum.stem.stop.Ratio D.T.cosmet D.nwrds.log D.nwrds.unq.log
## 1704 1.0000000 0 0.000000 0.000000
## 1299 1.0000000 0 0.000000 0.000000
## 1358 1.0000000 0 0.000000 0.000000
## 792 1.0000000 0 0.000000 0.000000
## 794 0.9884365 0 2.302585 2.079442
## 438 1.0000000 0 0.000000 0.000000
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1704 0.000000 0.000000 0.000000 0.000000
## 1299 0.000000 0.000000 0.000000 0.000000
## 1358 0.000000 0.000000 0.000000 0.000000
## 792 0.000000 0.000000 0.000000 0.000000
## 794 4.926312 0.547368 4.174387 4.007333
## 438 0.000000 0.000000 0.000000 0.000000
## D.ndgts.log D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log
## 1704 0 0 0 0 0
## 1299 0 0 0 0 0
## 1358 0 0 0 0 0
## 792 0 0 0 0 0
## 794 0 0 0 0 0
## 438 0 0 0 0 0
## D.npnct08.log D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log
## 1704 0 0 0 0.000000 0
## 1299 0 0 0 0.000000 0
## 1358 0 0 0 0.000000 0
## 792 0 0 0 0.000000 0
## 794 0 0 0 1.098612 0
## 438 0 0 0 0.000000 0
## D.npnct15.log D.npnct16.log D.npnct24.log D.nstopwrds.log
## 1704 0 0 0.0000000 0.000000
## 1299 0 0 0.0000000 0.000000
## 1358 0 0 0.0000000 0.000000
## 792 0 0 0.0000000 0.000000
## 794 0 0 0.6931472 1.098612
## 438 0 0 0.0000000 0.000000
## D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black D.P.white
## 1704 1.0 0 0 0 0
## 1299 1.0 0 0 0 0
## 1358 1.0 0 0 0 0
## 792 1.0 0 0 0 0
## 794 0.3 0 0 0 0
## 438 1.0 0 0 0 0
## .clusterid .clusterid.fctr startprice.predict.Final.glmnet
## 1704 1 1 230.6733
## 1299 1 1 296.5291
## 1358 1 1 247.3611
## 792 1 1 205.7933
## 794 1 1 209.6628
## 438 1 1 311.5721
## startprice.predict.Final.glmnet.err
## 1704 359.3267
## 1299 353.4709
## 1358 347.6389
## 792 344.2067
## 794 315.3372
## 438 310.5721sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.Interact.X.glmnet.importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## .rnorm 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.ndgts.log 57.154652
## D.npnct01.log 57.154652
## D.npnct03.log 57.154652
## D.npnct06.log 57.154652
## D.npnct08.log 57.154652
## D.npnct14.log 57.154652
## D.npnct24.log 57.154652
## D.nuppr.log 57.154652
## D.nwrds.log 57.154652
## D.nwrds.unq.log 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.sum.TfIdf 57.154652
## D.terms.n.post.stem.log 57.154652
## D.terms.n.post.stop.log 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+ 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## storage.fctr64 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000
## importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## .rnorm 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.ndgts.log 57.154652
## D.npnct01.log 57.154652
## D.npnct03.log 57.154652
## D.npnct06.log 57.154652
## D.npnct08.log 57.154652
## D.npnct14.log 57.154652
## D.npnct24.log 57.154652
## D.nuppr.log 57.154652
## D.nwrds.log 57.154652
## D.nwrds.unq.log 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.sum.TfIdf 57.154652
## D.terms.n.post.stem.log 57.154652
## D.terms.n.post.stop.log 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+ 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## storage.fctr64 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000
## Final.glmnet.importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## .rnorm 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.ndgts.log 57.154652
## D.npnct01.log 57.154652
## D.npnct03.log 57.154652
## D.npnct06.log 57.154652
## D.npnct08.log 57.154652
## D.npnct14.log 57.154652
## D.npnct24.log 57.154652
## D.nuppr.log 57.154652
## D.nwrds.log 57.154652
## D.nwrds.unq.log 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.sum.TfIdf 57.154652
## D.terms.n.post.stem.log 57.154652
## D.terms.n.post.stop.log 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+ 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## storage.fctr64 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 53
## UniqueID
## 1704 11705
## 1299 11300
## 1358 11359
## 792 10792
## 794 10794
## description
## 1704
## 1299
## 1358
## 792
## 794 Excellent Used Condition. Please see all photos and description.
## biddable startprice condition cellular carrier color storage
## 1704 0 590 New Unknown Unknown White 16
## 1299 1 650 New 1 Unknown Gold 128
## 1358 0 595 New Unknown Unknown Unknown Unknown
## 792 1 550 Used 0 None Gold 128
## 794 1 525 Used 0 None White 128
## productline .src .grpid .rnorm idseq.my prdline.my
## 1704 Unknown Train <NA> -0.9839429 1705 Unknown
## 1299 iPad Air 2 Train <NA> 1.3370014 1300 iPadAir
## 1358 Unknown Train <NA> -0.3914823 1359 Unknown
## 792 iPad Air 2 Train <NA> -0.2098595 792 iPadAir
## 794 iPad Air 2 Train <NA> -2.3482830 794 iPadAir
## startprice.log
## 1704 6.380123
## 1299 6.476972
## 1358 6.388561
## 792 6.309918
## 794 6.263398
## descr.my
## 1704
## 1299
## 1358
## 792
## 794 Excellent Used Condition. Please see all photos and description.
## condition.fctr cellular.fctr carrier.fctr color.fctr storage.fctr
## 1704 New Unknown Unknown White 16
## 1299 New 1 Unknown Gold 128
## 1358 New Unknown Unknown Unknown Unknown
## 792 Used 0 None Gold 128
## 794 Used 0 None White 128
## prdline.my.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1704 Unknown 0 0.000000
## 1299 iPadAir 0 0.000000
## 1358 Unknown 0 0.000000
## 792 iPadAir 0 0.000000
## 794 iPadAir 7 2.079442
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1704 0.000000 0 0.000000
## 1299 0.000000 0 0.000000
## 1358 0.000000 0 0.000000
## 792 0.000000 0 0.000000
## 794 4.983944 7 2.079442
## D.TfIdf.sum.post.stem D.terms.n.stem.stop.Ratio
## 1704 0.000000 1
## 1299 0.000000 1
## 1358 0.000000 1
## 792 0.000000 1
## 794 4.926312 1
## D.TfIdf.sum.stem.stop.Ratio D.T.cosmet D.nwrds.log D.nwrds.unq.log
## 1704 1.0000000 0 0.000000 0.000000
## 1299 1.0000000 0 0.000000 0.000000
## 1358 1.0000000 0 0.000000 0.000000
## 792 1.0000000 0 0.000000 0.000000
## 794 0.9884365 0 2.302585 2.079442
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1704 0.000000 0.000000 0.000000 0.000000
## 1299 0.000000 0.000000 0.000000 0.000000
## 1358 0.000000 0.000000 0.000000 0.000000
## 792 0.000000 0.000000 0.000000 0.000000
## 794 4.926312 0.547368 4.174387 4.007333
## D.ndgts.log D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log
## 1704 0 0 0 0 0
## 1299 0 0 0 0 0
## 1358 0 0 0 0 0
## 792 0 0 0 0 0
## 794 0 0 0 0 0
## D.npnct08.log D.npnct11.log D.npnct12.log D.npnct13.log D.npnct14.log
## 1704 0 0 0 0.000000 0
## 1299 0 0 0 0.000000 0
## 1358 0 0 0 0.000000 0
## 792 0 0 0 0.000000 0
## 794 0 0 0 1.098612 0
## D.npnct15.log D.npnct16.log D.npnct24.log D.nstopwrds.log
## 1704 0 0 0.0000000 0.000000
## 1299 0 0 0.0000000 0.000000
## 1358 0 0 0.0000000 0.000000
## 792 0 0 0.0000000 0.000000
## 794 0 0 0.6931472 1.098612
## D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black D.P.white
## 1704 1.0 0 0 0 0
## 1299 1.0 0 0 0 0
## 1358 1.0 0 0 0 0
## 792 1.0 0 0 0 0
## 794 0.3 0 0 0 0
## .clusterid .clusterid.fctr startprice.predict.Final.glmnet
## 1704 1 1 230.6733
## 1299 1 1 296.5291
## 1358 1 1 247.3611
## 792 1 1 205.7933
## 794 1 1 209.6628
## startprice.predict.Final.glmnet.err .label
## 1704 359.3267 11705
## 1299 353.4709 11300
## 1358 347.6389 11359
## 792 344.2067 10792
## 794 315.3372 10794
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "startprice.predict.Final.glmnet"
## [2] "startprice.predict.Final.glmnet.err"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 290.461 296.315 5.854
## 16 predict.data.new 9 0 296.315 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
# startprice.pred stuff
tmp_allobs_df <- glb_get_predictions(glb_allobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 1282 11283
## 2632 12634
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 1282 0 948.98 New 1 Unknown
## 2632 0 700.00 Used 1 Verizon
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 1282 Gold 128 iPad mini 3 Test <NA> -0.3303767 1283
## 2632 Unknown 32 iPad 2 Test <NA> 0.8127608 2634
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 1282 iPadmini 2+ 6.855388
## 2632 iPad 2 6.551080
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 1282 New 1 Unknown Gold
## 2632 Used 1 Verizon Unknown
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 7
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 1282 128 iPadmini 2+ 0
## 2632 32 iPad 2 7
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.079442 8.846628 7
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 1282 0.000000 0.000000 0
## 2632 2.079442 6.429203 7
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.079442 7.656131
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 1282 0.000000 0.000000
## 2632 2.079442 6.340152
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.cosmet
## 2623 1 0.8654292 0
## 1396 1 1.0000000 0
## 1418 1 1.0000000 0
## 2501 1 1.0000000 0
## 1282 1 1.0000000 0
## 2632 1 0.9861490 0
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 2623 2.944439 2.079442 7.656131 0.4253406
## 1396 0.000000 0.000000 0.000000 0.0000000
## 1418 0.000000 0.000000 0.000000 0.0000000
## 2501 0.000000 0.000000 0.000000 0.0000000
## 1282 0.000000 0.000000 0.000000 0.0000000
## 2632 2.484907 2.079442 6.340152 0.5763775
## D.nchrs.log D.nuppr.log D.ndgts.log D.npnct01.log D.npnct03.log
## 2623 4.634729 4.356709 1.098612 0 0
## 1396 0.000000 0.000000 0.000000 0 0
## 1418 0.000000 0.000000 0.000000 0 0
## 2501 0.000000 0.000000 0.000000 0 0
## 1282 0.000000 0.000000 0.000000 0 0
## 2632 4.077537 3.713572 1.609438 0 0
## D.npnct05.log D.npnct06.log D.npnct08.log D.npnct11.log D.npnct12.log
## 2623 0 0.6931472 0 0.6931472 0
## 1396 0 0.0000000 0 0.0000000 0
## 1418 0 0.0000000 0 0.0000000 0
## 2501 0 0.0000000 0 0.0000000 0
## 1282 0 0.0000000 0 0.0000000 0
## 2632 0 0.0000000 0 0.0000000 0
## D.npnct13.log D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log
## 2623 1.098612 0 0 0.6931472 0.6931472
## 1396 0.000000 0 0 0.0000000 0.0000000
## 1418 0.000000 0 0 0.0000000 0.0000000
## 2501 0.000000 0 0 0.0000000 0.0000000
## 1282 0.000000 0 0 0.0000000 0.0000000
## 2632 1.098612 0 0 0.0000000 0.6931472
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black
## 2623 2.302585 0.5263158 1 0 0
## 1396 0.000000 1.0000000 0 0 0
## 1418 0.000000 1.0000000 0 0 0
## 2501 0.000000 1.0000000 0 0 0
## 1282 0.000000 1.0000000 0 0 0
## 2632 1.098612 0.2500000 0 0 0
## D.P.white .clusterid .clusterid.fctr .lcn
## 2623 0 2 2 OOB
## 1396 0 1 1 OOB
## 1418 0 1 1 OOB
## 2501 0 1 1 OOB
## 1282 0 1 1 OOB
## 2632 0 1 1 OOB
## startprice.predict.All.Interact.X.glmnet
## 2623 133.68160
## 1396 195.47496
## 1418 83.96559
## 2501 276.73079
## 1282 373.46076
## 2632 158.99268
## startprice.predict.All.Interact.X.glmnet.err
## 2623 866.3084
## 1396 803.5250
## 1418 616.0344
## 2501 603.2592
## 1282 575.5192
## 2632 541.0073
## startprice.predict.All.Interact.X.glmnet.accurate
## 2623 FALSE
## 1396 FALSE
## 1418 FALSE
## 2501 FALSE
## 1282 FALSE
## 2632 FALSE
## startprice.predict.Final.glmnet startprice.predict.Final.glmnet.err
## 2623 133.68160 866.3084
## 1396 195.47496 803.5250
## 1418 83.96559 616.0344
## 2501 276.73079 603.2592
## 1282 373.46076 575.5192
## 2632 158.99268 541.0073rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_allobs_df <- tmp_allobs_df[, c(glb_id_var, glb_rsp_var, rsp_var_out)]
names(tmp_allobs_df)[3] <- glb_rsp_var_out
write.csv(tmp_allobs_df, paste0(glb_out_pfx, "predict.csv"), row.names=FALSE)
##
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 1282 11283
## 2632 12634
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 1282 0 948.98 New 1 Unknown
## 2632 0 700.00 Used 1 Verizon
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 1282 Gold 128 iPad mini 3 Test <NA> -0.3303767 1283
## 2632 Unknown 32 iPad 2 Test <NA> 0.8127608 2634
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 1282 iPadmini 2+ 6.855388
## 2632 iPad 2 6.551080
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 1282 New 1 Unknown Gold
## 2632 Used 1 Verizon Unknown
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 7
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 1282 128 iPadmini 2+ 0
## 2632 32 iPad 2 7
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.079442 8.846628 7
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 1282 0.000000 0.000000 0
## 2632 2.079442 6.429203 7
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.079442 7.656131
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 1282 0.000000 0.000000
## 2632 2.079442 6.340152
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.cosmet
## 2623 1 0.8654292 0
## 1396 1 1.0000000 0
## 1418 1 1.0000000 0
## 2501 1 1.0000000 0
## 1282 1 1.0000000 0
## 2632 1 0.9861490 0
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 2623 2.944439 2.079442 7.656131 0.4253406
## 1396 0.000000 0.000000 0.000000 0.0000000
## 1418 0.000000 0.000000 0.000000 0.0000000
## 2501 0.000000 0.000000 0.000000 0.0000000
## 1282 0.000000 0.000000 0.000000 0.0000000
## 2632 2.484907 2.079442 6.340152 0.5763775
## D.nchrs.log D.nuppr.log D.ndgts.log D.npnct01.log D.npnct03.log
## 2623 4.634729 4.356709 1.098612 0 0
## 1396 0.000000 0.000000 0.000000 0 0
## 1418 0.000000 0.000000 0.000000 0 0
## 2501 0.000000 0.000000 0.000000 0 0
## 1282 0.000000 0.000000 0.000000 0 0
## 2632 4.077537 3.713572 1.609438 0 0
## D.npnct05.log D.npnct06.log D.npnct08.log D.npnct11.log D.npnct12.log
## 2623 0 0.6931472 0 0.6931472 0
## 1396 0 0.0000000 0 0.0000000 0
## 1418 0 0.0000000 0 0.0000000 0
## 2501 0 0.0000000 0 0.0000000 0
## 1282 0 0.0000000 0 0.0000000 0
## 2632 0 0.0000000 0 0.0000000 0
## D.npnct13.log D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log
## 2623 1.098612 0 0 0.6931472 0.6931472
## 1396 0.000000 0 0 0.0000000 0.0000000
## 1418 0.000000 0 0 0.0000000 0.0000000
## 2501 0.000000 0 0 0.0000000 0.0000000
## 1282 0.000000 0 0 0.0000000 0.0000000
## 2632 1.098612 0 0 0.0000000 0.6931472
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black
## 2623 2.302585 0.5263158 1 0 0
## 1396 0.000000 1.0000000 0 0 0
## 1418 0.000000 1.0000000 0 0 0
## 2501 0.000000 1.0000000 0 0 0
## 1282 0.000000 1.0000000 0 0 0
## 2632 1.098612 0.2500000 0 0 0
## D.P.white .clusterid .clusterid.fctr startprice.predict.Final.glmnet
## 2623 0 2 2 133.68160
## 1396 0 1 1 195.47496
## 1418 0 1 1 83.96559
## 2501 0 1 1 276.73079
## 1282 0 1 1 373.46076
## 2632 0 1 1 158.99268
## startprice.predict.Final.glmnet.err
## 2623 866.3084
## 1396 803.5250
## 1418 616.0344
## 2501 603.2592
## 1282 575.5192
## 2632 541.0073if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 53
## UniqueID
## 2623 12625
## 1396 11397
## 1418 11419
## 2501 12503
## 1282 11283
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 1396 0 999.00 Used 0 None
## 1418 1 700.00 Used Unknown Unknown
## 2501 1 879.99 New 0 None
## 1282 0 948.98 New 1 Unknown
## color storage productline .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown Test <NA> -0.9259777 2625
## 1396 Unknown 32 iPad mini Test <NA> -0.1429904 1397
## 1418 Unknown Unknown Unknown Test <NA> 0.7258252 1419
## 2501 Space Gray 128 iPad Air 2 Test <NA> 1.7466852 2503
## 1282 Gold 128 iPad mini 3 Test <NA> -0.3303767 1283
## prdline.my startprice.log
## 2623 iPadmini 6.907745
## 1396 iPadmini 6.906755
## 1418 Unknown 6.551080
## 2501 iPadAir 6.779911
## 1282 iPadmini 2+ 6.855388
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 1396
## 1418
## 2501
## 1282
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 2501 New 0 None Space Gray
## 1282 New 1 Unknown Gold
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 7
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 2501 128 iPadAir 0
## 1282 128 iPadmini 2+ 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.079442 8.846628 7
## 1396 0.000000 0.000000 0
## 1418 0.000000 0.000000 0
## 2501 0.000000 0.000000 0
## 1282 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.079442 7.656131
## 1396 0.000000 0.000000
## 1418 0.000000 0.000000
## 2501 0.000000 0.000000
## 1282 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.cosmet
## 2623 1 0.8654292 0
## 1396 1 1.0000000 0
## 1418 1 1.0000000 0
## 2501 1 1.0000000 0
## 1282 1 1.0000000 0
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 2623 2.944439 2.079442 7.656131 0.4253406
## 1396 0.000000 0.000000 0.000000 0.0000000
## 1418 0.000000 0.000000 0.000000 0.0000000
## 2501 0.000000 0.000000 0.000000 0.0000000
## 1282 0.000000 0.000000 0.000000 0.0000000
## D.nchrs.log D.nuppr.log D.ndgts.log D.npnct01.log D.npnct03.log
## 2623 4.634729 4.356709 1.098612 0 0
## 1396 0.000000 0.000000 0.000000 0 0
## 1418 0.000000 0.000000 0.000000 0 0
## 2501 0.000000 0.000000 0.000000 0 0
## 1282 0.000000 0.000000 0.000000 0 0
## D.npnct05.log D.npnct06.log D.npnct08.log D.npnct11.log D.npnct12.log
## 2623 0 0.6931472 0 0.6931472 0
## 1396 0 0.0000000 0 0.0000000 0
## 1418 0 0.0000000 0 0.0000000 0
## 2501 0 0.0000000 0 0.0000000 0
## 1282 0 0.0000000 0 0.0000000 0
## D.npnct13.log D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log
## 2623 1.098612 0 0 0.6931472 0.6931472
## 1396 0.000000 0 0 0.0000000 0.0000000
## 1418 0.000000 0 0 0.0000000 0.0000000
## 2501 0.000000 0 0 0.0000000 0.0000000
## 1282 0.000000 0 0 0.0000000 0.0000000
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air D.P.black
## 2623 2.302585 0.5263158 1 0 0
## 1396 0.000000 1.0000000 0 0 0
## 1418 0.000000 1.0000000 0 0 0
## 2501 0.000000 1.0000000 0 0 0
## 1282 0.000000 1.0000000 0 0 0
## D.P.white .clusterid .clusterid.fctr startprice.predict.Final.glmnet
## 2623 0 2 2 133.68160
## 1396 0 1 1 195.47496
## 1418 0 1 1 83.96559
## 2501 0 1 1 276.73079
## 1282 0 1 1 373.46076
## startprice.predict.Final.glmnet.err .label
## 2623 866.3084 12625
## 1396 803.5250 11397
## 1418 616.0344 11419
## 2501 603.2592 12503
## 1282 575.5192 11283
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11943,
12050, 12115, 12253, 12285, 12367, 12388, 12585)
for (obs_id in glb_force_prediction_lst[["0"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11990, 11991, 11994, 11999,
12000, 12002, 12021, 12065, 12072,
12111, 12114, 12126, 12152, 12172,
12213, 12214, 12233, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
}
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 >= 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 <= 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
if (nrow(tmp_newobs_df) > 0)
stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y"), "startprice"]) >
max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
(glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
stop("startprice for some +ve predictions > 675")
}
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post_stem_words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_txt_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words TfIDf terms for descr.my:"
## TfIdf term freq pos cor.y cor.y.abs
## condit 209.3578 condit 496 109 -0.0370979676 0.0370979676
## use 148.2548 use 291 483 0.0145826883 0.0145826883
## scratch 129.1148 scratch 286 391 -0.0075325507 0.0075325507
## new 126.4193 new 156 299 -0.0385730073 0.0385730073
## good 121.7207 good 197 202 -0.0002501726 0.0002501726
## ipad 108.9895 ipad 232 235 -0.0123064552 0.0123064552
## TfIdf term freq pos cor.y cor.y.abs
## top 22.759846 top 24 466 0.04593910 0.04593910
## sold 14.062043 sold 13 423 -0.02192234 0.02192234
## regular 4.394223 regular 5 375 -0.02422602 0.02422602
## insid 3.465569 insid 3 228 0.02031203 0.02031203
## titl 2.075117 titl 2 463 NA NA
## pair 1.625083 pair 1 327 NA NA
## TfIdf term freq pos cor.y cor.y.abs
## 511 1.421948 511 1 11 -0.02152502 0.02152502
## attach 1.421948 attach 1 48 0.02500407 0.02500407
## binder 1.421948 binder 1 63 -0.02152502 0.02152502
## 360 1.263954 360 1 9 0.02500407 0.02500407
## 975 1.137558 975 1 15 NA NA
## 79in 1.034144 79in 1 14 -0.02152502 0.02152502
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - spacegray, great condition comes with the
## [1] 47
## [1] " Top_n post_stem_words TfIDf terms for descr.my:"
## [1] TfIdf term freq pos cor.y cor.y.abs top_n
## <0 rows> (or 0-length row.names)if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.Interact.X.glmnet"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.glmnet"print(dim(glb_fitobs_df))## [1] 860 59print(dsp_models_df)## model_id min.RMSE.fit max.R.sq.fit
## 17 All.Interact.X.glmnet 89.67939 6.324610e-01
## 11 All.X.glmnet 89.92115 5.761356e-01
## 13 All.X.no.rnorm.rf 91.20519 8.913483e-01
## 7 Low.cor.X.lm 91.52464 5.956499e-01
## 19 All.Interact.X.no.rnorm.rf 92.59660 8.974081e-01
## 3 Max.cor.Y.cv.0.cp.0.rpart 93.36670 4.923724e-01
## 10 All.X.bayesglm 93.87814 6.056393e-01
## 8 All.X.lm 95.96146 6.065530e-01
## 9 All.X.glm 95.96146 6.065530e-01
## 6 Interact.High.cor.Y.lm 96.61314 4.736677e-01
## 5 Max.cor.Y.lm 97.12892 4.594170e-01
## 16 All.Interact.X.bayesglm 102.73398 7.011685e-01
## 18 All.Interact.X.no.rnorm.rpart 106.43636 3.302237e-01
## 15 All.Interact.X.glm 109.52912 7.035928e-01
## 14 All.Interact.X.lm 109.52912 7.035928e-01
## 12 All.X.no.rnorm.rpart 111.83847 3.121279e-01
## 4 Max.cor.Y.rpart 111.83847 3.121279e-01
## 1 MFO.lm 131.03995 7.226357e-05
## 2 Max.cor.Y.cv.0.rpart 131.04468 0.000000e+00
## max.Adj.R.sq.fit
## 17 NA
## 11 NA
## 13 NA
## 7 0.569595158
## 19 NA
## 3 NA
## 10 NA
## 8 0.574343836
## 9 NA
## 6 0.463678006
## 5 0.454975555
## 16 NA
## 18 NA
## 15 NA
## 14 0.618841627
## 12 NA
## 4 NA
## 1 -0.001093153
## 2 NAif (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, glb_rsp_var,
predct_error_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "error.abs.OOB"
sOOB_ctgry_df <- dplyr::group_by(tmp_OOBobs_df, prdline.my)
sOOB_ctgry_df <- dplyr::count(sOOB_ctgry_df,
startprice.OOB.sum = sum(startprice),
err.abs.OOB.sum = sum(error.abs.OOB),
err.abs.OOB.mean = mean(error.abs.OOB))
names(sOOB_ctgry_df)[4] <- ".n.OOB"
sOOB_ctgry_df <- dplyr::ungroup(sOOB_ctgry_df)
#intersect(names(glb_ctgry_df), names(sOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, sOOB_ctgry_df, all=TRUE)
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}## [1] "All.Interact.X.glmnet OOB RMSE: 136.6729"
## .n.OOB prdline.my .n.Tst .freqRatio.Tst .freqRatio.OOB
## 7 340 iPadAir 340 0.1892042 0.1892042
## 2 205 Unknown 205 0.1140790 0.1140790
## 3 219 iPadmini 2+ 219 0.1218698 0.1218698
## 5 289 iPad 3+ 289 0.1608236 0.1608236
## 4 260 iPadmini 260 0.1446856 0.1446856
## 6 295 iPad 2 295 0.1641625 0.1641625
## 1 189 iPad 1 189 0.1051753 0.1051753
## startprice.OOB.sum err.abs.OOB.sum err.abs.OOB.mean
## 7 143765.13 47878.685 140.81966
## 2 41689.51 24575.775 119.88183
## 3 73254.02 25164.300 114.90548
## 5 73469.35 30158.279 104.35391
## 4 50772.66 18993.006 73.05002
## 6 47365.96 17339.800 58.77898
## 1 19462.87 9601.912 50.80377
## [1] "Final.glmnet prediction stats for glb_newobs_df:"
## model_id max.R.sq.new min.RMSE.new
## 1 Final.glmnet 0.5880457 136.6729if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print(glb_OOBobs_df[(glb_OOBobs_df$prdline.my == "iPadAir") &
!(glb_OOBobs_df[, predct_accurate_var_name]),
c(glb_id_var, glb_rsp_var_raw,
#"description"
"biddable", "startprice", "condition"
)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.Interact.X.glmnet.importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## .rnorm 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.ndgts.log 57.154652
## D.npnct01.log 57.154652
## D.npnct03.log 57.154652
## D.npnct06.log 57.154652
## D.npnct08.log 57.154652
## D.npnct14.log 57.154652
## D.npnct24.log 57.154652
## D.nuppr.log 57.154652
## D.nwrds.log 57.154652
## D.nwrds.unq.log 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.sum.TfIdf 57.154652
## D.terms.n.post.stem.log 57.154652
## D.terms.n.post.stop.log 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+ 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## storage.fctr64 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000
## importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## .rnorm 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.ndgts.log 57.154652
## D.npnct01.log 57.154652
## D.npnct03.log 57.154652
## D.npnct06.log 57.154652
## D.npnct08.log 57.154652
## D.npnct14.log 57.154652
## D.npnct24.log 57.154652
## D.nuppr.log 57.154652
## D.nwrds.log 57.154652
## D.nwrds.unq.log 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.sum.TfIdf 57.154652
## D.terms.n.post.stem.log 57.154652
## D.terms.n.post.stop.log 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+ 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## storage.fctr64 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000
## Final.glmnet.importance
## D.TfIdf.sum.stem.stop.Ratio 100.000000
## prdline.my.fctriPadmini 2+:storage.fctrUnknown 92.266354
## prdline.my.fctriPadmini 2+:D.npnct01.log 87.726579
## prdline.my.fctriPad 3+:color.fctrSpace Gray 83.082166
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details) 80.884362
## prdline.my.fctriPadAir:D.npnct01.log 80.398776
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 77.606708
## prdline.my.fctriPadAir 75.435335
## condition.fctrNew 75.402477
## prdline.my.fctriPad 1:storage.fctrUnknown 75.179542
## prdline.my.fctriPadmini:condition.fctrSeller refurbished 74.592174
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished 71.213231
## prdline.my.fctriPadAir:color.fctrWhite 70.171199
## prdline.my.fctriPadAir:color.fctrUnknown 68.394819
## prdline.my.fctriPad 2:.clusterid.fctr2 67.464181
## prdline.my.fctriPad 3+:condition.fctrNew other (see details) 67.441719
## prdline.my.fctriPadmini 2+:condition.fctrNew 67.352463
## prdline.my.fctriPadAir:condition.fctrNew 67.241286
## prdline.my.fctriPadAir:D.npnct16.log 67.209904
## prdline.my.fctriPadAir:D.npnct08.log 66.988519
## prdline.my.fctriPad 1:D.npnct16.log 66.792098
## prdline.my.fctriPadAir:condition.fctrNew other (see details) 66.698561
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 66.635896
## prdline.my.fctriPadAir:storage.fctr64 66.547363
## prdline.my.fctriPadmini 2+ 66.280538
## condition.fctrNew other (see details) 64.917952
## prdline.my.fctriPadAir:color.fctrGold 64.378893
## color.fctrSpace Gray 64.209695
## D.npnct16.log 62.755495
## prdline.my.fctriPadmini 2+:color.fctrWhite 62.718417
## color.fctrWhite 62.371835
## prdline.my.fctriPad 3+:storage.fctrUnknown 62.109398
## carrier.fctrOther 61.948224
## cellular.fctr1:carrier.fctrOther 61.932022
## cellular.fctr1:carrier.fctrUnknown 61.770404
## carrier.fctrT-Mobile 60.859986
## cellular.fctr1:carrier.fctrT-Mobile 60.807537
## prdline.my.fctriPad 2:D.npnct01.log 60.280380
## prdline.my.fctriPadmini 2+:storage.fctr64 60.011031
## prdline.my.fctriPadAir:color.fctrSpace Gray 59.790260
## prdline.my.fctriPad 2:condition.fctrFor parts or not working 59.570854
## prdline.my.fctriPad 1:biddable 59.568424
## prdline.my.fctriPadmini 2+:color.fctrSpace Gray 59.427860
## prdline.my.fctriPadmini 2+:D.nstopwrds.log 59.162950
## prdline.my.fctriPad 1:color.fctrSpace Gray 58.791868
## prdline.my.fctriPadmini:color.fctrWhite 58.756073
## cellular.fctr1 58.457035
## carrier.fctrVerizon 58.380382
## cellular.fctr1:carrier.fctrVerizon 58.367367
## D.nstopwrds.log 58.306431
## prdline.my.fctriPad 3+:storage.fctr64 58.164969
## color.fctrGold 58.116218
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 57.592616
## D.npnct12.log 57.312389
## D.terms.n.post.stop 57.250816
## D.terms.n.post.stem 57.226613
## .rnorm 57.154652
## D.TfIdf.sum.post.stem 57.154652
## D.TfIdf.sum.post.stop 57.154652
## D.nchrs.log 57.154652
## D.ndgts.log 57.154652
## D.npnct01.log 57.154652
## D.npnct03.log 57.154652
## D.npnct06.log 57.154652
## D.npnct08.log 57.154652
## D.npnct14.log 57.154652
## D.npnct24.log 57.154652
## D.nuppr.log 57.154652
## D.nwrds.log 57.154652
## D.nwrds.unq.log 57.154652
## D.ratio.nstopwrds.nwrds 57.154652
## D.sum.TfIdf 57.154652
## D.terms.n.post.stem.log 57.154652
## D.terms.n.post.stop.log 57.154652
## carrier.fctrNone 57.154652
## carrier.fctrUnknown 57.154652
## cellular.fctr1:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrNone 57.154652
## cellular.fctrUnknown:carrier.fctrOther 57.154652
## cellular.fctrUnknown:carrier.fctrSprint 57.154652
## cellular.fctrUnknown:carrier.fctrT-Mobile 57.154652
## cellular.fctrUnknown:carrier.fctrVerizon 57.154652
## condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr2 57.154652
## prdline.my.fctrUnknown:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr2 57.154652
## prdline.my.fctriPad 1:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 1:D.nchrs.log 57.154652
## prdline.my.fctriPad 1:D.npnct01.log 57.154652
## prdline.my.fctriPad 1:D.npnct08.log 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 1:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 1:color.fctrGold 57.154652
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 57.154652
## prdline.my.fctriPad 1:condition.fctrNew 57.154652
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 2:D.nchrs.log 57.154652
## prdline.my.fctriPad 2:D.npnct08.log 57.154652
## prdline.my.fctriPad 2:D.npnct16.log 57.154652
## prdline.my.fctriPad 2:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 2:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 2:biddable 57.154652
## prdline.my.fctriPad 2:color.fctrGold 57.154652
## prdline.my.fctriPad 2:color.fctrSpace Gray 57.154652
## prdline.my.fctriPad 2:condition.fctrNew 57.154652
## prdline.my.fctriPad 2:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPad 2:idseq.my 57.154652
## prdline.my.fctriPad 2:storage.fctr16 57.154652
## prdline.my.fctriPad 2:storage.fctr32 57.154652
## prdline.my.fctriPad 3+ 57.154652
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.154652
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 57.154652
## prdline.my.fctriPad 3+:D.nchrs.log 57.154652
## prdline.my.fctriPad 3+:D.npnct08.log 57.154652
## prdline.my.fctriPad 3+:D.nstopwrds.log 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPad 3+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPad 3+:biddable 57.154652
## prdline.my.fctriPad 3+:color.fctrGold 57.154652
## prdline.my.fctriPad 3+:color.fctrWhite 57.154652
## prdline.my.fctriPad 3+:condition.fctrNew 57.154652
## prdline.my.fctriPad 3+:idseq.my 57.154652
## prdline.my.fctriPad 3+:storage.fctr16 57.154652
## prdline.my.fctriPad 3+:storage.fctr32 57.154652
## prdline.my.fctriPadAir:.clusterid.fctr2 57.154652
## prdline.my.fctriPadAir:D.nstopwrds.log 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadAir:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished 57.154652
## prdline.my.fctriPadAir:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini 2+:D.nchrs.log 57.154652
## prdline.my.fctriPadmini 2+:D.npnct08.log 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini 2+:color.fctrGold 57.154652
## prdline.my.fctriPadmini 2+:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished 57.154652
## prdline.my.fctriPadmini 2+:idseq.my 57.154652
## prdline.my.fctriPadmini 2+:storage.fctr32 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr2 57.154652
## prdline.my.fctriPadmini:.clusterid.fctr3 57.154652
## prdline.my.fctriPadmini:D.nchrs.log 57.154652
## prdline.my.fctriPadmini:D.npnct08.log 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stem 57.154652
## prdline.my.fctriPadmini:D.terms.n.post.stop 57.154652
## prdline.my.fctriPadmini:biddable 57.154652
## prdline.my.fctriPadmini:color.fctrGold 57.154652
## prdline.my.fctriPadmini:color.fctrUnknown 57.154652
## prdline.my.fctriPadmini:condition.fctrNew 57.154652
## prdline.my.fctriPadmini:condition.fctrNew other (see details) 57.154652
## prdline.my.fctriPadmini:idseq.my 57.154652
## prdline.my.fctriPadmini:storage.fctr32 57.154652
## prdline.my.fctriPadmini:storage.fctr64 57.154652
## prdline.my.fctriPadmini:storage.fctrUnknown 57.154652
## storage.fctr64 57.154652
## idseq.my 57.152323
## prdline.my.fctriPad 1:idseq.my 57.150659
## prdline.my.fctriPad 1:D.nstopwrds.log 57.145417
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished 57.135914
## storage.fctrUnknown 57.135322
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished 56.680796
## color.fctrUnknown 56.593187
## prdline.my.fctriPadmini:D.nstopwrds.log 56.443494
## prdline.my.fctriPad 1:color.fctrUnknown 56.225972
## prdline.my.fctriPad 2:storage.fctr64 56.162243
## prdline.my.fctriPad 1:storage.fctr32 56.038084
## prdline.my.fctriPadmini 55.897662
## prdline.my.fctriPadAir:D.nchrs.log 55.770401
## prdline.my.fctriPadmini:D.npnct01.log 55.723742
## prdline.my.fctriPad 1:storage.fctr16 55.497810
## D.npnct13.log 55.479224
## D.npnct15.log 55.404298
## prdline.my.fctriPadmini:storage.fctr16 55.207883
## D.npnct11.log 55.183059
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 54.473731
## prdline.my.fctriPadmini:condition.fctrFor parts or not working 54.233303
## prdline.my.fctriPad 2:condition.fctrNew other (see details) 54.120749
## prdline.my.fctriPad 3+:color.fctrUnknown 53.841042
## prdline.my.fctriPad 2 53.553625
## prdline.my.fctriPadmini:color.fctrSpace Gray 53.482924
## condition.fctrSeller refurbished 53.219640
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 53.010406
## prdline.my.fctriPad 2:color.fctrWhite 52.953829
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished 52.806174
## prdline.my.fctriPadmini:D.npnct16.log 52.742671
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished 52.645795
## cellular.fctrUnknown:carrier.fctrUnknown 52.415510
## cellular.fctrUnknown 52.378418
## D.ratio.sum.TfIdf.nwrds 52.177701
## cellular.fctr1:carrier.fctrSprint 52.036570
## prdline.my.fctriPad 1:color.fctrWhite 51.994442
## carrier.fctrSprint 51.914487
## prdline.my.fctriPad 1:storage.fctr64 51.882493
## prdline.my.fctriPad 3+:.clusterid.fctr2 51.760390
## storage.fctr32 50.983973
## prdline.my.fctriPadmini 2+:biddable 50.900219
## prdline.my.fctriPad 2:storage.fctrUnknown 50.658047
## D.terms.n.stem.stop.Ratio 50.631526
## prdline.my.fctriPad 3+:D.npnct16.log 50.372138
## prdline.my.fctriPad 3+:D.npnct01.log 49.991675
## prdline.my.fctriPadAir:storage.fctr16 49.817157
## prdline.my.fctriPad 2:color.fctrUnknown 49.609570
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working 49.560538
## prdline.my.fctriPad 1:condition.fctrNew other (see details) 48.413227
## prdline.my.fctriPadAir:.clusterid.fctr3 48.126941
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working 47.029668
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 46.880973
## prdline.my.fctriPad 1 46.685518
## prdline.my.fctriPadAir:storage.fctr32 46.326109
## prdline.my.fctriPadmini 2+:storage.fctr16 46.284745
## storage.fctr16 46.080688
## condition.fctrFor parts or not working 44.113137
## prdline.my.fctriPadAir:biddable 43.080274
## D.npnct05.log 39.611184
## prdline.my.fctriPadmini 2+:D.npnct16.log 33.426232
## prdline.my.fctriPadAir:condition.fctrFor parts or not working 32.672853
## prdline.my.fctriPadAir:condition.fctrSeller refurbished 21.002570
## biddable 15.991702
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished 8.334719
## prdline.my.fctriPadAir:storage.fctrUnknown 0.000000print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 296.315 306.427 10.112
## 17 display.session.info 10 0 306.428 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 132.159 261.598 129.439
## 5 extract.features 3 0 17.380 107.851 90.471
## 12 fit.models 7 2 261.598 280.590 18.993
## 10 fit.models 7 0 116.618 132.159 15.541
## 16 predict.data.new 9 0 296.315 306.427 10.112
## 13 fit.models 7 3 280.591 287.381 6.790
## 15 fit.data.training 8 1 290.461 296.315 5.854
## 7 manage.missing.data 4 1 109.012 112.831 3.819
## 8 select.features 5 0 112.831 116.123 3.292
## 14 fit.data.training 8 0 287.381 290.460 3.079
## 1 import.data 1 0 10.773 13.837 3.064
## 2 inspect.data 2 0 13.838 16.067 2.230
## 6 cluster.data 4 0 107.851 109.011 1.160
## 3 scrub.data 2 1 16.068 16.782 0.714
## 4 transform.data 2 2 16.782 17.380 0.598
## 9 partition.data.training 6 0 116.123 116.617 0.494
## duration
## 11 129.439
## 5 90.471
## 12 18.992
## 10 15.541
## 16 10.112
## 13 6.790
## 15 5.854
## 7 3.819
## 8 3.292
## 14 3.079
## 1 3.064
## 2 2.229
## 6 1.160
## 3 0.714
## 4 0.598
## 9 0.494
## [1] "Total Elapsed Time: 306.427 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] randomForest_4.6-10 glmnet_2.0-2 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-43
## [7] rpart.plot_1.5.2 rpart_4.1-10 tidyr_0.2.0
## [10] entropy_1.2.1 dynamicTreeCut_1.62 proxy_0.4-15
## [13] reshape2_1.4.1 sqldf_0.4-10 RSQLite_1.0.0
## [16] DBI_0.3.1 tm_0.6-2 NLP_0.1-8
## [19] stringr_1.0.0 gsubfn_0.6-6 proto_0.3-10
## [22] mgcv_1.8-7 nlme_3.1-121 dplyr_0.4.2
## [25] plyr_1.8.3 gdata_2.17.0 doMC_1.3.3
## [28] iterators_1.0.7 foreach_1.4.2 doBy_4.5-13
## [31] survival_2.38-3 caret_6.0-52 ggplot2_1.0.1
## [34] lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.0 gtools_3.5.0 assertthat_0.1
## [4] digest_0.6.8 slam_0.1-32 R6_2.1.0
## [7] BradleyTerry2_1.0-6 chron_2.3-47 stats4_3.2.1
## [10] coda_0.17-1 evaluate_0.7 lazyeval_0.1.10
## [13] minqa_1.2.4 SparseM_1.6 car_2.0-25
## [16] nloptr_1.0.4 rmarkdown_0.7 labeling_0.3
## [19] splines_3.2.1 munsell_0.4.2 compiler_3.2.1
## [22] htmltools_0.2.6 nnet_7.3-10 codetools_0.2-14
## [25] brglm_0.5-9 gtable_0.1.2 magrittr_1.5
## [28] formatR_1.2 scales_0.2.5 stringi_0.5-5
## [31] RColorBrewer_1.1-2 tools_3.2.1 abind_1.4-3
## [34] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [37] knitr_1.10.5 quantreg_5.11